Paid Coding Courses

File Name:  
Keerti Purswani - Agentic AI System Design
Content Source:  
https://educosys.com/course/agentic-ai-system-design
Genre / Category:
Courses for Developers
Language:
ENGLISH
Original Price:  
₹35,399

Description:
- Core Objective: To teach learners how to design, build, and scale production-grade AI systems utilizing Retrieval-Augmented Generation (RAG), autonomous agents, memory management, evaluation frameworks, and LLMOps.
- Core Philosophy: Shifts the focus from basic prompt engineering to systemic thinking—designing robust agents, workflows, and architectures for real-world, enterprise-level applications.
2. Target Audience
- Software Development Engineers, AI Practitioners, and System Architects who want to move beyond theoretical buzzwords and gain hands-on expertise in building, architecting, and deploying scalable agentic AI systems.
3. Detailed Curriculum Breakdown
The course is structured into 5 comprehensive, progressive modules:
Module 1: Foundation + Knowledge Systems
- Retrieval fundamentals: embeddings, chunking, and vector search.
- Lexical, semantic, and hybrid retrieval techniques.
- Vector databases and nearest-neighbor algorithms (ANN, FAISS, HNSW).
- Production RAG architecture and advanced RAG patterns.
- Metadata filtering, access control, and multi-tenant retrieval.
- RAG debugging, optimization, and evaluation (RAG Evals).
Module 2: Context Engineering
- Context architecture and dynamic context assembly strategies.
- Implementation of short-term and long-term memory (including semantic and user preference memory).
- Token budgeting and context optimization.
- Context compression, summarization, and selective context loading.
- Strategies for managing stale or conflicting context.
Module 3: Agent Systems & Harness Engineering
- Agent architectures and execution patterns.
- Harness engineering: building skills and secure execution environments.
- Tool calling, function orchestration, and MCP (Model Context Protocol) interoperability.
- Tool reliability and fallback handling mechanisms.
- Multi-agent architectures, human-in-the-loop approvals, and state persistence.
Module 4: Production AI Engineering / LLMOps
- Managing long-running agentic workflows.
- Observability, tracing, and debugging in production environments.
- Building automated evaluation pipelines.
- LLMOps and release management workflows.
- Cost optimization, token economics, prompt caching, and semantic caching.
- Model routing, model ensembles, system reliability, security, and guardrails.
Module 5: End-to-End System Design Capstones
- Hands-on design and implementation of an AI coding agent.
- Production architecture discussions applied to real-world AI applications.
4. Key Takeaways & Practical Outcomes
- Systemic Mental Model: Learn to approach AI problems by designing complete systems, agents, and workflows rather than just focusing on isolated prompts.
- Hands-on Project Experience: Build complex, multi-agent applications. A highlighted example is building a SaaS Infrastructure Agent (a multi-agent CLI) that can:
* Design infrastructure directly from natural language requirements.
* Generate and validate Terraform and Docker artifacts.
* Monitor the generated infrastructure and publish artifacts to GitHub.
* Support long-term memory, human approvals, and resumable workflows.
- Production Readiness: Gain practical expertise in making AI systems reliable, observable, cost-effective, and secure for real-world deployment.

File Name:  
Keerti Purswani - Hands-On Generative AI
Content Source:  
https://educosys.com/course/hands-on-generative-ai
Genre / Category:
Courses for Developers
Language:
ENGLISH
Original Price:  
₹29,999

Description:
Learn generative artificial intelligence from scratch to an advanced level. Master creating, training, fine-tuning, and deploying modern AI models, developing intelligent agents, and creating comprehensive applications based on large language models (LLM).
The course combines the theoretical foundations of machine learning with practical development. You will study neural networks, generative models, transformer architecture, RAG technologies, vector databases, and modern AI development tools, including TensorFlow, Hugging Face, LangChain, LangGraph, and Ollama.
Practical assignments are given special attention. You will independently implement a transformer from scratch, train generative models, master fine-tuning LLMs, and develop your own AI applications, including an intelligent chatbot with memory, internet search, and the ability to work with external data sources.
What You Will Learn
Understand the mathematical foundations of artificial intelligence and the principles of neural networks.
Develop and train generative models GAN and VAE using TensorFlow.
Understand the architecture of transformers, attention mechanisms, and the structure of large language models GPT and BERT.
Independently implement transformers and apply pre-trained language models.
Fine-tune LLM with LoRA, QLoRA, Hugging Face, and Unsloth.
Create AI agents and automated workflows with LangChain and LangGraph.
Work with vector databases and develop RAG systems.
Create comprehensive AI applications with an interface on Streamlit.
Apply modern technologies MCP, Ollama, Mixture of Experts, and multimodal models.
Develop and deploy your own projects based on generative artificial intelligence.
Course Program
1. Fundamentals of Generative Artificial Intelligence
Start with the fundamental principles of artificial intelligence, the mathematical foundations of machine learning, and the operation of neural networks.
Module Topics:
Introduction to artificial intelligence.
Mathematical foundations of AI.
Probability theory, statistics, and linear algebra.
Basic principles of neural network operation.
Gradient descent and optimization methods.
Neural network architectures: Feedforward, RNN, and CNN.
Practical Projects:
Create a simple neural network using TensorFlow.
Train an autoencoder on the MNIST dataset.
2. Deep Generative Models
Explore the main architectures of generative artificial intelligence and learn to create models capable of generating new data.
Module Topics:
Differences between discriminative and generative models.
Generative adversarial networks (GAN).
Variational autoencoders (VAE).
Probabilistic data generation using VAE.
Develop generative models in TensorFlow.
Visualize metrics and training results with TensorBoard.
Practical Projects:
Four mini-projects using TensorFlow.
Implement GANs for generating handwritten digits.
Train VAE for generating face images based on the CelebA dataset.
3. Transformers and Large Language Models (LLM)
Understand the architecture underlying modern language models. Study attention mechanisms and independently implement a transformer.
Module Topics:
Recurrent neural networks RNN and LSTM.
Transformer architecture.
Attention mechanisms: Self-Attention and Positional Encoding.
Encoder-Decoder architecture.
Pre-training tasks: Masked Language Modeling (MLM) and Causal Language Modeling (CLM).
Architectures and features of GPT and BERT models.
Practical Projects:
Major project: implement a transformer from scratch.
Text sentiment analysis using BERT.
4. Fine-tuning LLM, LangChain, and LangGraph
Learn to adapt pre-trained language models for specific tasks and create intelligent systems using modern frameworks.
Module Topics:
Pre-training and fine-tuning language models.
Efficient fine-tuning methods LoRA and QLoRA.
Working with Hugging Face libraries and models.
Adapting language models for text summarization and answering questions.
LangChain installation and configuration.
Main components of LangChain: Prompts, Memory, Chains, and Agents.
LangGraph: nodes, states, StateGraph, and workflows.
Develop AI agents.
Practical Project:
Creating a question-answering application using LangChain.
5. Vector Databases and RAG
Master Retrieval-Augmented Generation (RAG) — a technology that allows language models to use information from external sources to form responses.
Learn to work with vector databases, create RAG pipelines, and develop comprehensive AI applications with a user interface.
Module Topics:
Principles of vector databases.
Using ChromaDB.
Architecture and practical application of RAG.
Building RAG pipelines with LangChain.
Developing user interfaces with Streamlit.
Integrating LLM with external tools and information sources.
Practical Projects:
Project 1. Creating a comprehensive AI chatbot similar to ChatGPT.
Develop an intelligent chatbot using Streamlit, LangGraph, ChromaDB, and large language models. Implement dialogue memory, web search tool integration, and the ability to use external data.
Project 2. Multimodal AI application.
Create an application on Streamlit combining several capabilities of generative artificial intelligence:
Image generation.
Automatic image description creation.
Generating textual descriptions of video materials.
6. Modern Technologies and Architectures of Generative AI
Get acquainted with current tools, protocols, and architectural approaches used in the development of modern AI systems.
Module Topics:
Model Context Protocol (MCP): connecting AI models to external tools and data sources.
Ollama: local deployment and use of language models.
Fine-tuning models using Unsloth.
Mixture of Experts (MoE) architecture.
Chain of Thought: methods for organizing multi-step reasoning in language models.
DeepSeek architecture.
Practical Projects:
Fine-tuning language models using Unsloth.
Practical application of studied tools and architectures.
7. Advanced Methods and Multimodal Artificial Intelligence
The final module is dedicated to modern approaches to the development and optimization of generative models, as well as technologies for processing various types of data.
Module Topics:
Model distillation (Model Distillation).
Diffusion models (Diffusion Models).
Visual transformers (Vision Transformers).
Multimodal artificial intelligence models.
CLIP architecture.
Prompt engineering.
Practical projects using the studied technologies.
Learning Outcome
By the end of the course, you will understand not only the principles of modern generative models but the entire process of AI application development: from the mathematical foundations and training neural networks to creating intelligent agents and deploying comprehensive solutions based on LLM.
Practical projects will help consolidate the acquired knowledge and develop the skills necessary for independent development of generative models, RAG systems, AI chatbots, and multimodal applications.

File Name:  
Keerti Purswani - HHLD - Hands-On High Level System Design Implementation
Content Source:  
https://educosys.com/course/hands-on-high-level-system-design-implementation
Genre / Category:
Courses for Developers
Language:
ENGLISH
Original Price:  
₹12,499

Description:
Practical High-Level System Design is a course on system design, microservice architecture, and designing distributed systems, aimed at preparing for technical interviews and solving real engineering tasks. Instead of learning architecture only from diagrams, you will design, implement, connect, and deploy full-fledged services using AWS, Docker, Kubernetes, Kafka, databases, and Node.js.
About the Course
The program is based around practice: you will first understand the basic components of cloud infrastructure, and then move on to creating large-scale systems, similar in architectural challenges to a messenger like WhatsApp, a video platform like YouTube, and a brokerage platform like Zerodha.
The course helps you understand how solutions from High-Level System Design turn into working applications: from choosing storage and message queues to setting up networks, deploying microservices, monitoring, and observability.
Who the Course is For
Backend developers who want to move from developing individual services to designing entire systems.
Engineers preparing for System Design and High-Level Design interviews.
Developers who want to understand AWS, Kubernetes, Kafka, Docker, and microservice infrastructure in practice.
Specialists who need to understand scalability, fault tolerance, data storage, queues, CDN, and service interaction.
What You Will Learn
Designing distributed systems at a high level.
Microservices architecture and service interaction.
Using AWS components to build production-like infrastructure.
Working with databases, object storage, CDN, DNS, and network infrastructure.
Using Kafka and Amazon MSK for asynchronous event exchange.
Containerizing and deploying services with Docker and Kubernetes.
Approaches to logging, monitoring, diagnosing, and observing systems.
Course Program
Week 1 — Basics of AWS and Setting Up the Environment
In the first stage, you will be introduced to key services and technologies that are used in building modern scalable systems. This part lays the foundation for subsequent practical projects.
EC2 Instances for launching computing resources.
Security Groups for managing network access.
IAM Roles for setting up permissions and secure access.
VPC for organizing network infrastructure.
DynamoDB for NoSQL storage.
RDS for working with relational databases.
S3 for object data storage.
CDN and CloudFront for speeding up content delivery.
Kafka and Amazon MSK for event architecture.
DNS and Route 53 for domain management and routing.
Docker for application containerization.
Kubernetes for container orchestration.
Basics of Node.js for service implementation.
Integrating infrastructure components using Node.js.
Weeks 2–3 — WhatsApp-level Messaging System
In this module, you will design and implement a scalable messaging system. The project will help you understand how high-load applications with a large number of users and constant data exchange are built.
Within the project, you will:
analyze the High-Level Design architecture of the messenger;
design the main services and their zones of responsibility;
implement the necessary microservices and components;
set up interaction between services;
work through message storage and event transmission;
combine the separate parts of the system;
deploy the complete solution in the cloud.
Weeks 4–5 — YouTube-level Video Streaming Platform
The next project is about designing a platform for video storage, processing, and streaming. You will understand how systems are designed where scalability, content delivery speed, and reliable media file storage are important.
In this module, you will:
analyze the architecture of a video streaming platform;
design upload, storage, and video delivery services;
study the role of object storage and CDN;
implement microservice components;
integrate the infrastructure into a unified system;
deploy the project.
Weeks 6–7 — Zerodha-level Brokerage System
The third major project is about designing a platform for stock market operations. This type of system requires special attention to request processing speed, data integrity, events, and fault tolerance.
You will analyze the High-Level Design of a brokerage platform, implement the key microservices, set up their interaction, integrate components, and deploy the complete solution. Special attention is paid to architectural decisions that may be discussed during system design interviews.
Week 8 — Common Components of Distributed Systems
The final part of the course focuses on infrastructural components essential to practically any production system. You will learn how to track the state of services, identify problems, and maintain the operability of a distributed application.
Service and event logging.
System state monitoring.
Problem diagnosis in distributed architecture.
Observability and analysis of service behavior.
Review typical questions and complexities that arise during development.
Practical Results After Training
After completing the course, you will have not only theoretical understanding of High-Level System Design but also practical experience in creating several complex distributed systems. You will be more confident in discussing architectural solutions during interviews and better understand how to design services for real-world loads.
Experience in designing systems comparable to a messenger, video platform, and brokerage application.
Understanding of microservice architecture and service interaction principles.
Skills in working with AWS, Docker, Kubernetes, Kafka, databases, and Node.js.
Understanding of scaling, data storage, network infrastructure, and content delivery.
Practice in deploying and integrating components into a single system.
Why Study High-Level System Design in Practice
In system design interviews, it is important not just to know the names of technologies, but to be able to explain compromises: which database to choose, where to use a message queue, how to scale a service, how to ensure fault tolerance, and how to monitor the system after launch.
This course helps link architectural concepts with real development. You will see how design decisions affect code, infrastructure, deployment, and application operation.

File Name:  
Build AI Agents that Never Sleep
Content Source:  
https://master.dev/workshops/background-agents/
Genre / Category:
Courses for Developers
Language:
ENGLISH
Original Price:  
$199

Description:
Practical Workshop on Creating Background AI Agents with Node.js, TypeScript, and Inngest will help you understand how to design agents that do not rely on an open browser tab, maintain state, wait for human decisions, and resume work after failures. You will build an agent to respond to incidents and see how such systems are applied in real-world automation, internal tools, and product scenarios.
What You Will Create in the Course
During the course, you will develop an AI agent that analyzes the state of a simulated service, chooses an appropriate way to resolve issues, requests confirmation for risky actions, checks recovery, and generates an incident report.
The main goal of the course is to demonstrate not just calling a language model but a full-fledged lifecycle of a background agent: task initiation, progress saving, waiting for external events, reassessing conditions, and safe continuation of execution.
The agent explores the state of the service and determines the possible cause of the incident.
The system checks the actions chosen by the model before execution.
Human confirmation is requested for potentially dangerous operations.
After a pause or restart, the agent continues the task from the saved state.
The final result is recorded in an incident report.
From a Simple Agent Cycle to a Reliable Background System
The work begins with a small agent cycle on Node.js and TypeScript. Then, you will add a management harness that is responsible for the system's state, verifying the actions chosen by the model, and confirming task completion.
This approach helps control AI agent behavior at the application level: setting execution rules, limiting risky operations, verifying results, and not relying solely on the language model's response.
State Preservation and Recovery After Failures
Next, you will connect Inngest to save the results of model calls and tools, pause execution until an event or human decision occurs, and safely restore work after errors.
You will examine how the agent continues the task after the application restart and why it's important to reassess the current state of the service before executing deferred actions. This is especially important for background AI agents that can operate for minutes, hours, or longer.
State Preservation and Recovery After Failures
Next, you will connect Inngest to save the results of model calls and tools, pause execution until an event or human decision occurs, and safely restore work after errors.
You will examine how the agent continues the task after the application restart and why it's important to reassess the current state of the service before executing deferred actions. This is especially important for background AI agents that can operate for minutes, hours, or longer.
Human-in-the-loop and Safe Execution
Special attention is given to scenarios where the agent should not act fully autonomously. You will learn how to request human approval, handle rejections, expiration of wait time, and situations where circumstances change during a pause.
This approach makes agent systems more predictable and applicable in production scenarios: incident response, DevOps process automation, internal tools, and workflows with increased control requirements.
What You Will Learn
Understand the difference between background execution and chat interfaces and when an AI agent needs a lifecycle with state preservation.
Create a management harness on Node.js that monitors state, verifies model actions, and confirms result achievement.
Use Inngest for reliable performance of long tasks and recovery after application restart.
Pause agent work until an event, timer, or human decision, and then recheck the conditions.
Implement human-in-the-loop for actions that may impact service operation.
Make retries safe using idempotency, timeouts, execution limits, and cancellations.
Test agent resilience through a local event simulator and Inngest tracing.
Adapt the course architecture for your own background processes, automation, and internal tools.

File Name:  
Agentic AI Masters 2026: LangChain, LangGraph & CrewAI
Content Source:  
https://www.udemy.com/course/agentic-ai-masters-langchain-langgraph-crewai-n8n/
Genre / Category:
Courses for Developers
Language:
ENGLISH
Original Price:  
$49

Description:
The Agentic AI Masters 2026 is a complete, end-to-end mastery program designed to take you from Python fundamentals all the way to building autonomous, production-ready AI agents.
This comprehensive course covers the entire modern AI stack. You will start with strong Python foundations and progress through classical NLP techniques (tokenization, stopwords, n-grams, Bag of Words, TF-IDF, POS tagging, NER), word embeddings (Word2Vec CBOW & SkipGram, GloVe, FastText), and deep learning architectures including ANNs, CNNs, RNNs, LSTMs, GRUs, and bi-directional models.
You will then master the revolutionary Transformer architecture (encoder-only, decoder-only, and full encoder-decoder) and dive deep into Large Language Models — understanding tokens, context windows, prompt engineering, prompt tuning, and working with leading models from OpenAI, xAI, and local deployment using Ollama.
The course features extensive coverage of Retrieval Augmented Generation (RAG), vector databases vs vector indexes, advanced RAG architectures, evaluation metrics, and RAG assessment frameworks. You will also learn model fine-tuning techniques and how to deploy generative AI applications in production.
In the Agentic AI specialization phase, you will master:
LangChain for building intelligent chains and agents
LangGraph for creating reliable, stateful, controllable multi-agent workflows
CrewAI for orchestrating role-based collaborative AI teams
Low-code automation using n8n
Guardrails for AI safety, validation, compliance, and output governance
LangSmith for tracing, debugging, evaluation, observability, and production monitoring
Advanced multi-agent systems design and orchestration
Throughout the program, you will work on multiple real-world projects that simulate industry use cases — from intelligent document Q&A systems and automated research agents to smart assistants, workflow automation, and complex multi-agent solutions.
By the end of this course, you will have the confidence and practical skills to design, develop, evaluate, and deploy sophisticated agentic AI systems that can reason, plan, use tools, and collaborate — making you job-ready for the rapidly growing demand in the agentic AI space.
Whether you are a developer, data scientist, AI engineer, or tech professional looking to future-proof your career, Agentic AI Masters 2026 delivers the deepest, most practical, and up-to-date education in building the next generation of AI agents.

File Name:  
MLOps Bootcamp: Build Real-World AI Infrastructure
Content Source:  
https://zerotomastery.io/courses/learn-mlops-engineering/
Genre / Category:
Courses for Developers
Language:
ENGLISH
Original Price:  
$249

Description:
MLOps Bootcamp is a hands-on course on building infrastructure for real-world AI systems: from model experimentation to deployment, monitoring, retraining, and controlled release of new versions into production. You will explore the full cycle of MLOps in AWS and build a working pipeline for a machine learning model that can be maintained, improved, and scaled.
About the Course
Training a model is only part of the task. For a machine learning model to be useful in a real application, it is necessary to prepare data, organize storage, deploy the model as a service, track prediction quality, and regularly update it with new examples. MLOps addresses these tasks, merging machine learning, data engineering, cloud infrastructure, and DevOps.
In this course, you will go from experimentation to a working AI service in AWS. You will fine-tune a Vision Transformer using Hugging Face and Amazon SageMaker, prepare data in Amazon S3, deploy the model through an endpoint for real-time predictions, and set up monitoring of its behavior in Amazon CloudWatch.
Special emphasis is placed on the automation of model improvements. You will develop a process where the system identifies predictions with low confidence, saves new data, sends it for labeling, and initiates retraining. This approach helps to transform the model from a one-time experiment into a sustainable ML product.
What You Will Create in Practice
During the course, you will build MLOps infrastructure for an AI application, where the model progresses from training to safe updates in production.
Prepare datasets and organize data storage in Amazon S3.
Fine-tune the Vision Transformer using Hugging Face and Amazon SageMaker.
Deploy the model as an endpoint for real-time inference.
Set up data collection on requests, model responses, and prediction confidence levels.
Connect model activity monitoring through Amazon CloudWatch.
Integrate AWS Lambda, API Gateway, S3, and Amazon Bedrock into a unified workflow.
Create an automated pipeline for data preparation, training, evaluation, and model deployment.
Set up quality checks for new model versions before releasing to production.
What You Will Learn
Understand the complete MLOps lifecycle: from experiments and training to monitoring, retraining, and model delivery to production.
Prepare data for ML projects in AWS cloud infrastructure.
Work with Amazon SageMaker for training, fine-tuning, and deploying machine learning models.
Use Hugging Face for fine-tuning modern computer vision models.
Deploy machine learning models as API services for processing real-time requests.
Track model performance, prediction confidence, and potential behavior degradation.
Create MLOps pipelines for automating retraining and evaluating new model versions.
Configure deployment conditions so that only versions passing quality checks are released to production.
Key Tools and Technologies
The course is built around tools used in modern cloud ML projects and MLOps infrastructure.
Amazon S3 - data storage for datasets, new examples, and model artifacts.
Amazon SageMaker - training, fine-tuning, evaluation, and deployment of ML models.
Hugging Face — working with pre-trained models and fine-tuning Vision Transformer.
Amazon CloudWatch - monitoring model activity, logs, and metrics.
AWS Lambda — automating individual steps in the MLOps process.
API Gateway — creating an API layer for service interaction.
Amazon Bedrock - leveraging generative AI capabilities in workflows.
AWS Pipelines — constructing a sequence of steps from data preparation to model release.
How the Learning Process is Structured
From Experiment to Production-Ready Model
You will start with data preparation and model fine-tuning, then move on to practical tasks that arise when transitioning an ML solution to real infrastructure: deployment, endpoint, monitoring, logging, and quality control.
Automating Retraining
The next stage involves building a model improvement process. You will set up the collection of low-confidence predictions, save new examples, label, and send data to the retraining pipeline. This will help understand how to organize continuous ML system improvement.
Controlled Release of New Versions
A new model should not go into production just because it was trained later. In the course, you will set up metric checks and deployment conditions to ensure only the version showing the best results and meeting quality requirements goes live.
Who the Course is For
ML engineers and data scientists aiming to bring models to production.
Python developers interested in infrastructure for AI services and machine learning.
DevOps engineers wanting to understand MLOps, SageMaker, and ML pipelines.
Data engineers working with cloud services and data processing pipelines.
Professionals seeking practical experience in building MLOps solutions on AWS.
Prerequisite Knowledge
For a comfortable course experience, it's advisable to understand the basics of machine learning, have basic Python experience, and be familiar with how APIs and cloud services work. Deep experience in AWS is not required, as key infrastructure components are explored in the context of a practical project.
Result After Completion
By the end of the course, you will understand how the components of modern MLOps infrastructure work together: data, model, endpoint, monitoring, automation, retraining, and controlled deployment. You will have practical experience building an AI application in AWS that can be maintained, improved, and scaled.
This course will help transition from isolated model experiments to an engineering approach where machine learning becomes part of a reliable production system.

File Name:  
Sanket Singh - Advanced Hands On System Design HLD + LLD Course
Content Source:  
https://courses.algocamp.io/learn/Advanced-System-Design-2025
Genre / Category:
Courses for Developers
Language:
ENGLISH
Original Price:  
₹12,499

Description:
🚀 Advanced Hands-On System Design (HLD + LLD) Course

Ready to crack system design interviews and become a true expert in large-scale distributed systems?
A comprehensive hands-on program designed for senior software engineers, aspiring architects, and tech leads.

---

🔥 What’s New in System Design 2.0?
• A more structured, deeper, and company-specific approach to mastering both HLD and LLD.
• Greater technical detail with emphasis on real interview patterns.
• FAANG-style problems and scenarios inspired by Meta, Google, Uber, and Netflix.

📚 What You’ll Learn:
✅ High-Level Design (HLD): Build scalable, reliable, and fault-tolerant systems from scratch.
✅ Low-Level Design (LLD): Master SOLID principles and key design patterns (Creational, Structural, Behavioral).
✅ Advanced Design Patterns: Saga, Choreography vs Orchestration, Anti-Corruption Layers, Backend-for-Frontend (BFF).
✅ Concurrency & Multithreading: Threading, deadlocks, locks vs non-blocking, task queues, producer-consumer patterns.

🧠 Projects & Assignments:
• 20+ Real HLD Problems: Social media Timeline, K-Heavy Hitters, Real-Time Alerts, etc.
• 20+ Take-Home Assignments: Yelp Clone, Notification Scheduling, Hotel Booking Systems, etc.
• LLD Challenges: BookMyShow, E-commerce Cart, Cab Matching, Rate Limiter, Splitwise, and more.

🔍 Core Topics Covered:
• Databases & Storage: Sharding, Indexing, Caching, Replication, CAP Theorem.
• Networking & Load Balancing: TCP/IP, CDNs, Load Balancer Internals, API Gateways.
• Async Systems: Kafka, RabbitMQ, Polling vs Webhooks, Event-driven designs.
• Big Data Pipelines & Security: Stream Processing, Data Lakes, Encryption, Rate Limiting, Circuit Breakers.

---

🔥 Course Syllabus Overview 🔥
(603 Lessons | 5 Quizzes)

## 🏗️ Low-Level Design (LLD)
• Introduction to LLD & Basics of Design Principles
• SOLID Principles (SRP, OCP, LSP, ISP) & Dependency Inversion
• Design Patterns: Prototype, Builder, Iterator, Repository, Adapter, Factory, State, Chain of Responsibility, Observer, Command
• Machine Coding Problem Approach & Interview Practice
• Real-World LLD Projects:
- ATM Machine Design
- PubSub System
- Parking Lot System
- BookMyShow Design
- E-commerce Platform & Criteria Design Pattern
- Tic Tac Toe & Chess (Interview Problems)
- Splitwise Design & Cap Table Generator
- Stock Broker / Stock Exchange (Spring Boot)
- Coinbase Interview Practice
• Concurrency, Threads, and Understanding Locks
• Logger Design Assignment

## 🌐 High-Level Design (HLD) Foundations
• System Design Terminologies & DBMS Basics Refresher
• Understanding Scalability & Availability
• Basics of REST API
• Databases for Scale: Transactions, Isolation Levels, Sharding, Replication
• AWS Services Deep Dive: EC2, ELB, S3, DynamoDB, Redshift, Kinesis, Glue
• Setting up Load Balancers manually (NginX)
• Asynchronous Communication: Kafka, Designing Leetcode
• Distributed Systems: 2 General & Byzantine Problems
• Important Numbers for Estimations

## 🏛️ Real-World HLD Case Studies
• URL Shortener Design (Interactive)
• Designing Uber Platform
• Job Scheduling Platform
• Hotel Reservation System
• Design Patterns for Scalable Systems
• Designing Google Search
• Live NewsFeed System (Twitter/Instagram)
• Consistency Models: CAP, Pacelc & PIE Theorem
• Designing Key-Value Store & Your Own Cache
• Design Tinder & Yelp (Assignments)
• Internals of LSM Trees (Design your own database)
• Understanding Clocks: Lamport clock, Vector clock, CRDT, G & PN Counter, Or Set
• Google Spanner Case Study: Understanding TrueTime
• Consensus Algorithms: Basics, RAFT (Simple), PAXOS (Legacy)
• Zippy DB: Geographically distributed KV store
• K-Heavy Hitter Design: Count-Min Sketch
• Distributed Transactions & Two-Phase Commit (2PC)
• Saga Pattern: Orchestration & Choreography, CQRS
• Elasticsearch Internals: TF-IDF & Inverted Indexes
• Collaborative Docs Design (like Google Docs)
• Cassandra Deep Dive
• Online Auction System & Web Crawler Design
• Time Series DB: Influx DB
• Multicast and MMAP using Stock Exchange
• Designing Dropbox / Google Drive
• Netflix: Design Movie Streaming Service
• Chat System Design / WhatsApp
• Change Data Capture (CDC) with Debezium
• DB Design Practice: Booking.com & Quora/Twitter
• Big Data Topics for System Design
• Designing Your Own Rate Limiter
• Designing Stock Broker (Zerodha Mock Interview)
• Designing Ad Click Platform & Notification Service
• Designing Google Meet (Assignment)

## 🛠️ Optional / Bonus Modules
• Concurrent Programming & Threads
• Understanding Testing Principles
• Networking for System Design
• Crash Course to Write API using FastAPI
• Advanced Features of Redis
• Problem Solving: Writing Simple Infra Scripts

---

ℹ️ Course Details:
• Target Audience: Senior Software Engineers, Aspiring Architects, Tech Leads
• Focus: FAANG-style HLD & LLD interview preparation, real-world distributed systems
• Format: Comprehensive hands-on program with live classes, assignments, and mock interviews

File Name:  
Sanket Singh - Complete Al Engineer Bootcamp 2026: LLM, RAG, AI Agents
Content Source:  
https://courses.algocamp.io/learn/AI-Course
Genre / Category:
Courses for Developers
Language:
ENGLISH
Original Price:  
₹13,999

Description:
🤖 AI Engineering — Beginner to Advanced Course

A 6-month, project-driven program to build, evaluate, deploy, and scale real-world AI products from scratch.

---

🛠️ HANDS-ON PROJECTS INCLUDED
📦 Working with the OpenAI API: Summarization, translation, tone control, ticket routing, code generation.
🏢 Customer Support Agent: Classifies tickets, retrieves help docs via RAG, drafts replies, and escalates to humans.
🧠 Exploring Embeddings: Semantic search, recommendation, and classification systems powered by vector spaces.
📺 RAG Chatbot over YouTube Transcripts: Pinecone-backed chatbot that retrieves relevant timestamps and answers questions.
🏭 Enterprise Advanced RAG: Hybrid search (BM25 + vector), cross-encoder reranking, guardrails, and RAGAS evaluation.
💻 Claude Code–like Coding Agent: Understands codebase, inspects files, makes edits, runs commands in a sandbox, and iterates.
🔍 AI Code Reviewer Agent (like CodeRabbit): Reads PRs, flags bugs/security issues with RAG, and posts structured inline comments.
🔈 Voice-Based AI Agent: Chained voice agent (STT → LLM → TTS) for natural conversations and tool calling.
📈 AI Trading Agent (Bonus): Ingests market data/news, reasons over signals via agentic RAG, and proposes structured trade ideas.
⚡ Simple Neural Network in TensorFlow: End-to-end model with forward pass, training loop, and evaluation.
✍️ MNIST Autoencoder: Unsupervised representation learning and reconstruction (first generative model).
🔤 Multi-Input Models for OCR: Fuses image and metadata streams in PyTorch for end-to-end OCR.
💬 ChatGPT-Style Chatbot Platform: Streamlit UI, persistent multi-turn memory, web search tools, and ChromaDB vector store.
📄 Chat With Any Document: ChatPDF-style app for research papers, contracts, and textbooks.
🔎 AI Search Engine (like Perplexity): Agentic web retrieval, hybrid search, inline citations, streaming answers, and faithfulness evaluation.
🪄 AI Web App Builder (like Lovable/v0): Prompt-to-app system with a multi-agent pipeline (planner → coder → reviewer).
🎤 AI Interview Taker (like Mercor): Conducts technical/behavioral interviews over voice, scores candidates, and generates hiring reports.

---

🚀 PART 1: AI Engineering First (Months 1 to 5)

🐍 0. Python Refresher for AI Engineering
• Python Basics, Data Handling, OOP, Error Handling, File Handling
• Type Hints, Pydantic Basics, Async Python, Python for APIs & AI Workflows
• NumPy, Pandas, and Tensors intuition

🧠 1. LLM Mental Model & API Setup
• Tokens, context windows, sampling, statelessness, and LLM API anatomy

💬 2. Conversations & First Chatbot
• Chat roles, system prompts, multi-turn conversations, and end-to-end chatbot building

✏️ 3. Prompt Engineering — Foundations to Advanced
• Structured outputs, zero/one/few-shot prompting, Chain-of-Thought, Auto-CoT, Context engineering

📝 4. Real-World Text Tasks & Chatbot Development
• Summarization, translation, code generation, ticket routing, and Customer Support Agent project

🗂️ 5. Structuring End-to-End LLM Applications
• JSON outputs, error handling, batching, rate-limit retries, and FastAPI basics for AI

🔧 6. Function Calling & Structured Outputs
• Function calling, parallel/forced calls, external API integration, Pydantic validation, OpenAI/Claude SDKs

🧲 7. Embeddings & Semantic Search
• Creating embeddings, vector space reasoning, semantic search, and classification

🗄️ 8. Vector Databases
• Vector DB theory, ChromaDB, Pinecone, Milvus, and YouTube transcript RAG project

🔍 9. Retrieval-Augmented Generation (RAG)
• RAG fundamentals, LangChain, LlamaIndex, hybrid search, reranking, query rewriting, RAGAS metrics

⚡ 10. Scalable RAG with Async Queues
• Sync vs async RAG, Python RQ with Redis/Valkey on Docker, FastAPI chat queue design

🤗 11. Hugging Face — Pre-Trained Models in Production
• Hugging Face Hub, AutoModels, AutoTokenizers, and Document Q&A pipelines

🎨 12. Multi-Modal AI with Hugging Face
• Computer vision, speech recognition, zero-shot classification (CLIP/CLAP), VQA, diffusion, and video models

🤖 13. AI Agents — From First Agent to Multi-Agent Systems
• Agentic AI fundamentals, custom tools, web search, agentic RAG, multi-agent coordination, AutoGen & CrewAI

🛤️ 14. LangGraph & Stateful Agent Workflows
• Nodes, edges, state, StateGraph, checkpointing with MongoDB, Claude Code-like Coding Agent project, LangFlow

🧠 15. The Memory Layer in AI Agents
• Short-term, long-term, factual, episodic, semantic memory, Mem0, vector-db-backed memory

🕸️ 16. Graph Memory & Knowledge Graphs
• Neo4j and Kuzu for graph storage, Cypher query basics, adding graph memory to agents

🎙️ 17. Multi-Modal Agents & Voice Agents
• Image-based reasoning, Speech-to-Speech voice agents, chained STT → LLM → TTS agents

🔌 18. Model Context Protocol (MCP) & Agent SDK
• MCP architecture, building tiny MCP servers, Agent SDK concepts, AI Code Reviewer Agent project

🔁 19. Streaming, Retries, Fallbacks & Rate Limits
• SSE streaming, exponential backoff, idempotency keys, token budgets, multi-provider LLM Gateway

🖥️ 20. Local LLM Deployment
• Ollama, Dockerized LLM environments, OpenWebUI, FastAPI services on top of Ollama

💰 21. Prompt Caching & Cost/Latency Optimization
• Prefix-based caching, KV cache fundamentals, batching, prefill, and decode phases

🔭 22. Observability & Tracing
• Langfuse, LangSmith, Braintrust, OpenLLMetry, OpenTelemetry GenAI semantic conventions

✅ 23. Evaluations — The Underrated AI Engineering Skill
• Trace-by-hand workflow, failure bucketing, LLM-as-judge prompts, judge alignment, RAGAS metrics

🔐 24. Security, Guardrails & Responsible Deployment
• OWASP Top 10 for LLMs, defense-in-depth, least-privilege tools, NeMo Guardrails, Llama Guard, prompt injection testing

⚙️ 25. MLOps & LLMOps in Production
• MLOps/LLMOps lifecycle, cost management, prompt compression, governance, RAG vs fine-tuning

🛡️ 26. Production Best Practices
• Moderation APIs, prompt-injection mitigation, output validation, adversarial testing, AI Trading Agent project

---

🧬 PART 2: Deep Learning Foundations (Month 6)

📊 27. Math & PyTorch Foundations
• Linear algebra, probability, calculus, gradient descent, PyTorch tensors, training loops, optimizers, MNIST Autoencoder

👁️ 28. Robust Deep Learning & Computer Vision
• OOP in PyTorch, Dataset/DataLoader classes, vanishing/exploding gradients, ReLU/ELU, Batch Normalization, CNNs

🔀 29. Sequence Modelling & Multi-IO Architectures
• RNNs, LSTMs, GRUs, forecasting, multi-input/output architectures, loss weighting, OCR project

🧩 30. Transformers from Scratch
• Self-attention, positional encoding, multi-head/masked attention, full Transformer architecture, custom BPE tokenizer

🎨 31. Deep Generative Models
• GAN fundamentals, Variational Autoencoders (VAEs), face-generation on CelebA, TensorBoard metrics

⚙️ 32. LLM Internals — Theory to Match the Practice
• KV Cache, Paged Attention, Flash Attention, Sparse Attention, Quantization, Mixture of Experts, RLHF, DeepSeek architecture

🎯 33. Fine-Tuning, LoRA/QLoRA & Trending Topics
• Pretraining vs fine-tuning, Parameter-efficient fine-tuning (PEFT), hands-on fine-tuning with Unsloth, Diffusion models, Vision Transformers

---

🌟 Outcome of the Course
By the end of this 6-month program, you will be able to:
• Build production-ready AI applications using LLM APIs
• Design and deploy RAG systems over real-world data
• Work with vector databases (ChromaDB, Pinecone, Milvus)
• Build AI agents with tools, memory, workflows, and state
• Create multi-modal and voice-based AI systems
• Deploy local and hosted LLM applications
• Add observability, tracing, evaluations, guardrails, and cost controls
• Understand the theory behind neural networks, CNNs, RNNs, Transformers, and LLMs
• Fine-tune models using modern techniques like LoRA and QLoRA
• Build a strong AI engineering portfolio with real-world projects

File Name:  
The complete Claude Ecosystem Bootcamp (All levels)
Content Source:  
https://code4startup.com/claude_ecosystem_bootcamp/
Genre / Category:
Courses for Developers
Language:
ENGLISH
Original Price:  
$297

Description:
Practical Course on the Claude Ecosystem is a step-by-step training on working with Anthropic AI tools: from initial prompts and account setup to developing AI agents, automating processes, and integrating Claude with external services. Over seven days, you will explore approaches, tools, and work systems based on more than 400 hours of practical use of Claude.
About This Course
The course helps transition from occasional dialogues with the neural network to systematic work with Claude in development, business, analytics, content, and everyday tasks. The main focus is on practice: creating effective requests, managing context, using Projects and Artifacts, connecting tools through Skills and MCP, working with Claude Code, and building your automated scenarios.
The program is structured to gradually increase in complexity: first, you master the basic logic of interacting with Claude, then move on to project work, integrations, code, agents, and complete workflows.
Who the Course is For
Developers who want to use Claude Code to analyze codebases, generate solutions, test, and speed up development.
Entrepreneurs and Managers who need AI automation for documents, tasks, research, reports, and internal processes.
Marketers and Content Creators who want to build a system for preparing, planning, and publishing materials using Claude.
Analysts and Data Specialists who need to connect AI to files, tables, databases, APIs, and external services.
Claude Users who are already familiar with neural networks but want to use them more professionally and systematically.
What You Will Learn in the Course
Setting up a Claude account, choosing a pricing plan, and working with available interfaces.
Creating effective prompts: role instructions, examples, constraints, formatting, and clarifying dialogues.
Working with contextual windows, memory, long documents, and multi-step tasks.
Using Claude Projects and Artifacts to organize materials, documents, code, prototypes, and dashboards.
Connecting tools, specialized skills, external services, and APIs through Skills, MCP, connectors, and plugins.
Automating actions in the browser, graphical interface, and work applications.
Working with Claude Code in the terminal: project exploration, making changes, running tests, and iterative refinement.
Creating AI agents and repeatable systems for business, content, administration, and analytics.
Course Program
Level 1. Basics of Working with Claude
The first block is devoted to the foundation: setting up the account, getting acquainted with the interface, and principles of effective communication with Claude. You will learn how to formulate requests, set the model's role, manage response style, and achieve predictable results.
Registration, profile setup, and choosing the appropriate tariff.
Differences between regular dialogue, projects, and specialized work modes.
Prompt-engineering for Claude: request structure, context, goal, constraints, and result criteria.
Multi-step dialogues, clarifications, response quality verification, and working with errors.
Managing context windows and memory constraints when working with large tasks.
Level 2. Tools, Projects, Artifacts, and Integrations
At the second level, you will learn to turn Claude into a work environment for projects. Instead of separate chats, you will organize materials, create documents, generate code, assemble prototypes, and connect external services.
Working with Claude Projects for storing instructions, files, research, and project context.
Using Artifacts for creating texts, code, interfaces, visualizations, and dashboards.
Connecting Skills for specialized tasks and repeatable actions.
Integrations through MCP: interacting with databases, APIs, file systems, and external tools.
Using Cowork, browser agents, connectors, and plugins for everyday tasks.
Level 3. Advanced Scenarios and Claude Code
The third block focuses on productive work with constraints, complex tasks, and programming. You will learn how to plan sessions, distribute loads, and use Claude Code for real projects.
Planning work sessions considering usage limits and task complexity.
Dividing large projects into stages, controlling progress, and recording intermediate results.
Automating actions in the graphical interface and browser.
Creating visual materials, interactive prototypes, and technical drafts.
Working with Claude Code in the terminal: repository analysis, error search, file changes, running tests, and refactoring.
Level 4. Custom Systems and AI Agents
The final level is dedicated to building custom systems based on Claude. You will learn to design modular AI processes where Claude interacts with files, tools, data, and external services.
Creating modular infrastructure for administrative tasks and regular operations.
Synchronizing data between services, documents, tables, and databases.
Developing agents for preparing, planning, and publishing content.
Setting up action chains: data collection, analysis, result generation, verification, and notification sending.
Designing reliable scenarios with constraints, logging, and quality control.
Separate Block: Market Analysis Automation
The course also covers automated scenarios for stock and cryptocurrency markets. You will learn how to use Claude for analyzing signals, news, sentiments, and historical data, as well as setting up notifications and testing hypotheses.
Collecting and structuring market information from various sources.
Analyzing sentiments, news, and key factors affecting assets.
Testing strategies on historical data and comparing results.
Setting up notifications and monitoring rules considering risk constraints.

File Name:  
From Zero to Hero: Microsoft.Extensions.AI in .NET
Content Source:  
https://dometrain.com/course/from-zero-to-hero-microsoft-extensions-ai-in-dotnet/
Genre / Category:
Courses for Developers
Language:
ENGLISH
Original Price:  
$129

Description:
Course on Microsoft.Extensions.AI is a practical guide that will help .NET developers confidently transition from AI experiments to creating full-fledged production solutions. You will master working with local and cloud-based LLMs, learn to build smart applications, integrate on-device AI, and apply modern approaches like RAG, vector search, and image generation.
What you will learn during the course
Working with LLM in .NET
You will understand how to use Microsoft.Extensions.AI to interact with models, manage prompts, and create intelligent chat clients.
Connecting Ollama and local models
Dialogue history and system instructions
Function calling
Evaluation and testing of AI responses
Retrieval-Augmented Generation (RAG)
You will learn to enhance model capabilities with your own data using modern search mechanics.
Creation and use of embeddings
Working with vector storages
Building reliable RAG pipelines
Image Generation
The course includes working with the IImageGenerator interface for creating images within .NET applications.
On-device AI and mobile capabilities
You will master the integration of local AI on devices using Microsoft.Maui.Essentials.AI and learn how to leverage Apple Intelligence features.
Deployment and cloud AI platforms
The final part is dedicated to bringing solutions to production.
Deployment through Microsoft Foundry
Integration with AWS Bedrock
Practices for building scalable AI services
Who this course is for
The course is designed for .NET developers who want to confidently work with modern AI tools, create smart interfaces, optimize workflows, and implement AI functionality into real products.
Skills you will acquire
Integration of LLM and multimodal models in .NET
Building RAG systems
Image generation and multimodal scenarios
Mobile on-device AI functions
Production deployment of AI solutions

File Name:  
Maven - Make the Case: How to Present Design Ideas That Spark Action
Content Source:  
https://maven.com/data-informed-design/makethecase
Genre / Category:
Courses for Developers
Language:
HINDI
Original Price:  
$999

Description:
Make the Case — a practical course on how to convincingly present design ideas, connect UX solutions with business goals, and achieve real actions from leaders, product managers, engineers, and other stakeholders.
What this course is about
A good design idea rarely leads to changes on its own. You may see a problem in the product before others, understand how to improve user experience, find growth points in the interface or customer journey — but if the proposal sounds simply like "we should fix this," it often goes unnoticed.
The course helps shift from a language of taste and subjective improvements to a language of results: metrics, business impact, risks, priorities, and evidence-based argumentation. You will learn to explain why a design initiative is important right now, what impact it can have on the product, and how to turn an idea into a solution that stakeholders support.
Special attention is given to situations where a company's UX maturity is low, data is incomplete, and designer's initiatives are not always seen as a priority. Instead of pressure, political games, and abstract requests, you will learn to build a clear case that helps people make a decision.
What you will learn
Formulate design ideas as business opportunities, not just visual or UX improvements.
Link user problems with product metrics, team goals, and business results.
Build compelling arguments for leaders, product managers, engineers, and other audiences.
Present initiatives that no one asked for and create a sense of priority without pressure.
Define success criteria and explain how the effect of the proposed solution will be measured.
Work with incomplete data and still construct logical, verifiable, and honest arguments.
Identify weak points in your presentation before meeting with stakeholders.
Use AI processes to prepare arguments, analyze data, and create presentation materials.
Key frameworks and approaches
The program uses practical tools that help structure thinking, assess the impact of ideas, and translate design solutions into clear business arguments.
HEART
This framework helps analyze user experience through categories such as satisfaction, engagement, adoption, retention, and task success. You will learn how to apply HEART to choose UX metrics and assess product quality.
Goals-Signals-Metrics
This approach helps transition from a general goal to specific user behavior signals and measurable metrics. It is particularly useful when you need to prove that a design change is linked to real results.
ICE
The ICE method is used for prioritizing initiatives based on impact, confidence, and ease of implementation. You will learn to compare ideas and choose those that can have the greatest effect at reasonable costs.
Problem-Agitate-Solution
This approach helps build a compelling story: first, show the problem, then enhance understanding of its consequences, and finally offer a solution. This structure makes the presentation clearer and motivates the audience to act.
Who is this course for
UX/UI designers who want to have a stronger influence on product decisions.
Product designers who need to argue initiatives through metrics and business value.
Designers in companies with low UX maturity who need to demonstrate the importance of user experience.
Specialists who want to transition from task execution to more strategic roles.
Design leads and senior designers who need to present ideas to management and cross-functional teams.
Those who want to work more confidently with stakeholders and promote initiatives without conflicts and pressure.
What problems the course helps solve
The course will be especially useful if you have encountered situations where good UX ideas did not reach implementation because they were perceived as "non-essential," "too costly," or "not now."
Your proposals are supported verbally but not included in the roadmap.
Management asks to prove the value of UX, but it’s unclear which data to use.
Product managers and engineers see design initiatives as additional workload.
You understand the user's problem but cannot connect it to business metrics.
Design presentations turn into discussions of taste instead of outcome discussions.
Ideas go unnoticed because they don’t seem urgent or prioritized.
What is included in the program
The course program is built around the practical task of learning how to turn a design observation or UX problem into a convincing case for decision-making.
Working with the problem
How to correctly formulate user and product problems.
How to distinguish a symptom from a cause.
How to show the consequences of the problem for business, team, and users.
Argumentation through data and metrics
How to select relevant metrics for design initiatives.
How to work if data is insufficient or incomplete.
How to formulate hypotheses and success criteria.
Presenting the idea to stakeholders
How to adapt the same argument for different audiences.
How to answer the question “Why should we tackle this now?”
How to identify objections in advance and strengthen weak points in the presentation.
Prioritization and choice of solutions
How to compare design initiatives based on potential impact.
How to use ICE and other approaches to choose priorities.
How to demonstrate the value of a solution without making inflated promises.
AI in preparing a design case
How to use AI to analyze materials and find arguments.
How to speed up presentation preparation and idea structuring.
How to verify the logic of argumentation using AI tools.

Author's expertise
The program is based on 13 years of the author's experience working in companies with low UX maturity, where designers often have to not only create solutions but also prove their business value.
The course is supplemented with insights from interviews with more than 30 design leaders from companies like Target, Ford, Mastercard, Meta, and Amazon. This helps to consider the problem of convincing stakeholders not only from the position of an individual designer but also from the perspective of design leaders who regularly make strategic decisions.
What result you will get
After completing the course, you will be able to present design ideas more confidently, explain their value, and elevate discussions from subjective opinions to the level of goals, data, and expected impact.
You will have a clear process for preparing a design case.
You will be able to link UX initiatives with business metrics and product goals.
Your presentations will become more structured and convincing.
You will learn to speak to stakeholders in the language of priorities, risks, and results.
You will be able to promote important initiatives even in an environment where UX is not yet a mature function.

File Name:  
Sanket Singh - Backend Development in Springboot, AI & Micro Services 2026
Content Source:  
https://courses.algocamp.io/learn/learn-spring-boot-backend-development
Genre / Category:
Courses for Developers
Language:
HINDI
Original Price:  
₹11,999

Description:
🚀 Ultimate Backend Development Course (Spring Boot Edition) 🚀

🔥 Top Highlights
This course goes beyond CRUD and teaches you how to build production-grade, cloud-native distributed systems using Spring Boot.

🧠 Most Advanced Concepts You'll Master:
✅ CQRS (Command Query Responsibility Segregation)
✅ SAGA Pattern: Both Orchestration (via orchestrators) and Choreography (event-based coordination)
✅ Event Sourcing with Kafka
✅ Transactional Outbox Pattern for guaranteed event publishing
✅ Microservices Architecture from scratch with real-world HLD & LLD
✅ API Gateway (with rate limiting, routing, auth, etc.)
✅ Service Discovery using Spring Cloud Netflix Eureka
✅ Distributed Transactions and system design strategies
✅ Consistency models - Eventual, Casual and Immediate
✅ DB Replication - Master slave, multi master, leaderless
✅ DB Transactions and DB Internals - LSM Trees, WAL based redo logs, db transaction and isolation levels, indexing
✅ DB Triggers and Change Data Capture (CDC)
✅ GeoHashing and Geohash based indexes - Comparison with quadtrees and using Geohashed DBs
✅ Distributed locks using REDIS - Comparison with pessimistic and optimistic locking

💻 Flagship Projects Included:
🔷 Major Projects (HLD + LLD + Code + Testing):
1. Uber-like Ride Hailing Backend
2. Payment Wallet System
3. Airbnb Clone – Stay Booking Platform
4. Stock Exchange - For order matching Like NSE

🔹 Minor Projects:
1. Quora Clone – Q&A and Social Features
2. Ecommerce application

🧱 Build Systems & Java Ecosystem
• Gradle Deep Dive: Build scripts, multi-module projects, creating fat jars, custom tasks, and Gradle CLI
• Creating custom annotations and leveraging AOP (Aspect-Oriented Programming) in Spring
• Introduction to Spring Initializr and project scaffolding
• Deep dive into JVM, GC, and performance tuning

🧠 Low-Level Design (LLD)
• Understanding Object-Oriented Principles in Java
• Core design patterns: Builder, Singleton, Strategy, Factory
• MVC Architecture in Spring
• Writing services, controllers, DTOs, and entities
• Service-repository patterns and layered architecture

🚀 Basics of Spring Boot
• Understanding API Development in Spring Boot using Spring Boot Web
• Integrating DB configuration using application.properties
• Creating API and their documentation using @OpenAPIDefinition
• Adding POJO with the help of Lombok
• Dependency Injection, Dependency Inversion, Autowiring

❤️ MVC Architecture | Preparing industry scale projects
• Making industry-ready project structure with MVC and modified MVC
• Service layer, Schema layer and repository layer
• Config layer, Seeders and Controllers, Routing layer, Jobs and consumers

🔌 REST API Development
• Building RESTful services with Spring MVC
• CRUD operations using Spring Boot
• Creating and managing Beans, Controllers, Services
• Exception handling using @ControllerAdvice
• Calling external APIs using RestTemplate

🧩 Project Architecture & Microservices
• Modular Monolith vs Microservices architecture
• Microservices with Spring Cloud, Feign Clients
• HLD of Ecommerce, Uber, and Wallet systems
• Service discovery, API Gateway, centralized configuration
• Synchronous & asynchronous communication strategies

🧵 Messaging & Distributed Systems
• Apache Kafka: Producers, Consumers, Topics, Partitions, Kafka integration with Spring Boot
• Saga Pattern: Managing distributed transactions
• Two-Phase Commit (2PC) basics
• CQRS: Command-Query Responsibility Segregation

⚙️ Authentication & Authorization
• Spring Security configuration, JWT-based authentication
• Role-based access control, OAuth 2.0 basics and securing microservices

📡 Real-Time Communication & Spring AI
• WebSockets using STOMP and SockJS for real-time chat/order updates
• Integrating and configuring LLM models with Spring AI

💰 Kafka & RabbitMQ
• Apache Kafka architecture and real-time data pipelines
• RabbitMQ architecture and message exchange patterns
• Dead letter queues, message retry mechanisms, and scaling high throughput
• Comparing Kafka vs RabbitMQ use cases and trade-offs

💸 Advanced Transactions in Databases
• ACID properties, atomicity, consistent and consistency patterns
• Strong vs eventual consistency, isolation levels and serialization
• Durability in DBs, Pessimistic & Optimistic Concurrency control

🧩 Detailed Projects Covered:
1. Quora App Backend: Complex schema setup, ODM queries, integrating Elastic Search for inverted index search.
2. Payment Wallet System (Paytm/Uber Wallet): Distributed transactions, SAGA using choreography and orchestrations.
3. Airbnb.com Backend: Microservices hotel booking, MySQL + MongoDB, JWT auth, idempotent APIs.
4. Ecommerce App: Monolithic implementation, basics of Spring Boot annotations, REST APIs.
5. Uber Backend: Geohashing, quad trees, scalable APIs, WebSockets for real-time driver/user notifications.
6. Stock Exchange for Order Matching: Strategy/builder patterns, in-memory order book, limit orders, multi-threading.

📚 Pre-requisites (Recorded content provided before live classes start)
• Basics of Java: Variables, OOPs, Error Handling, Collections
• Basic MySQL & MongoDB Queries: CRUD, JOINs, Aggregation, Indexing
• Basics of Computer Networks: OSI Model, TCP/IP, DNS, HTTP/HTTPS, Client-Server architecture

ℹ️ Course Details
• Format: Fully recorded course

File Name:  
Maven - AI Evals Certification: Master Building Reliable AI
Content Source:  
https://maven.com/ai-evals-and-analytics/ai-evals-analytics-playbook
Genre / Category:
Courses for Developers
Language:
ENGLISH
Original Price:  
$1,399

Description:
AI Evals Certification is a practical course on testing, evaluating the quality, and monitoring generative AI systems . This training helps build a systematic approach to AI Evals, identify weaknesses in LLM products, reduce risks before launch, and confidently improve AI systems after production release.
About the AI Evals Certification Course
The course focuses on evaluating and quality controlling modern GenAI products: chatbots, AI assistants, RAG systems, agent solutions, multimodal models, and other products based on large language models. Participants will learn how to design evals before release, select metrics, collect test datasets, and use evaluation results for making product and engineering decisions.
What You Will Learn
Build the AI Evals process within a product, ML, or QA team.
Determine quality criteria for LLM and GenAI systems considering business objectives.
Choose between manual, semi-automated, and automated evaluation of AI products.
Develop qualitative and quantitative metrics for evaluating model responses.
Create test datasets and scenarios to check AI system behavior.
Use the LLM-as-a-Judge approach and understand its limitations.
Track quality degradation, errors, hallucinations, and other failures post-release.
Apply online evals for monitoring AI systems in production.
Link evaluation results with product analytics and business impact.
Implement protective mechanisms, governance approaches, and risk controls.
Training Program
Basics of AI Evals and Quality of GenAI Products
The first part of the course covers principles of evaluating generative AI systems, typical errors of LLM products, and the role of evals in the development lifecycle. Participants will explore why standard QA is often insufficient for AI systems and how to design criteria for successful model behavior in advance.
Metrics, Criteria, and Test Datasets
This block is dedicated to developing metrics and evaluation sets. You will learn how to form datasets for testing, segment user scenarios, evaluate accuracy, completeness, relevance, safety, usefulness, and compliance of responses with expected requirements.
Manual and Automated Evaluation
The course helps understand when to use expert manual annotation, when automated checking is sufficient, and when a hybrid approach is needed. The advantages and limitations of automated evals are discussed separately, including evaluation using LLM-as-a-Judge.
Monitoring AI Systems in Production
Participants will learn how to monitor model behavior after launch: detect degradation, analyze user queries, control response quality, document incidents, and make decisions about rollback, refinement, or scaling of changes.
Evaluation-Driven Development
The practical part of the course introduces the Evaluation-Driven Development approach, where evaluation becomes part of AI product development. Testing results are used to prioritize improvements, verify hypotheses, control releases, and consistently enhance system quality.
AI Governance, Risks, and Security
A separate module is dedicated to risk management, requirements for safe AI implementation, control of undesirable model behavior, and creation of protective mechanisms. It also covers modern approaches to AI Governance and AI system quality at the organizational level.
Practical Part of the Course
During the training, participants complete a full practical project on AI Evals: from setting goals and choosing metrics to building the evaluation process and analyzing results. This format helps not only to study theory but also to apply it to real testing and AI product improvement tasks.
Develop quality criteria for AI systems.
Create test scenarios and an evaluation dataset.
Set up manual or automated evaluation.
Analyze results and identify problematic areas.
Prepare recommendations for model or product improvement.
Apply EDD for further development iterations.
Bonus Topics and Expert Analyzes
In addition to the main program, the course includes additional materials and expert lectures on current directions in AI system evaluation. Participants will learn about modern tools and approaches used in teams creating GenAI products.
LLM-as-a-Judge and features of using language models for response evaluation.
Multimodal evals for systems working with text, images, and other data formats.
Third-party vendor tools for monitoring, testing, and analyzing AI quality.
AI Governance and organizational processes for quality control.
Evaluation of business impact and association of AI Evals with product metrics.
Who the Course is Suitable For
Product Leaders and Product Managers are responsible for launching and developing GenAI products.
Data Scientists and ML Engineers who need to systematically evaluate model and AI pipeline quality.
QA and Test Engineers transitioning to testing LLM, AI assistants, and generative systems.
AI Engineers and Developers who implement evaluation in the development process.
Team Leaders who need to manage risks, quality, and reliability of AI solutions.
Results After Completing the Course
After training, you will be able to build a clear and reproducible process for evaluating AI systems: determine what needs to be measured, what data to use, how to interpret the results, and make decisions based on them.
Understand how to test LLM and GenAI products before and after release.
Design an eval system for specific business and user scenarios.
Identify hallucinations, irrelevant responses, unsafe behavior, and quality degradation.
Use metrics and analytics to improve AI products.
Organize monitoring of AI systems in an industrial environment.
Apply AI Evals as a constant quality improvement process, not a one-time check.
Why It's Important to Study AI Evals
Generative AI systems can behave unpredictably: provide inaccurate responses, change quality after model updates, perform poorly in certain query segments, or create business risks. Therefore, evaluating AI systems becomes a key stage in the development and operation of AI products.
AI Evals Certification helps acquire practical skills necessary for the safe launch, quality control, and continuous improvement of AI systems in real conditions.

File Name:  
ByteByteGo - Build with Claude Code
Content Source:  
https://bytebyteai.com/c/build-with-claude-code/
Genre / Category:
Courses for Developers
Language:
ENGLISH
Original Price:  
$499

Description:
"Building with Claude Code" — a practical intensive course designed to help you quickly and confidently master the official Anthropic CLI agent: from running initial commands to building autonomous multi-agent systems. The course is created for those who want to work faster, write cleaner, and use Claude at the engineering team level.
Who this course is for
Beginners: if you want to start learning Claude Code from scratch.
Experienced users: if you're already using the tool but want to reach an advanced level.
Architects and team leads: if you need to implement agent workflows and build multi-agent processes.
Practical engineers: if your goal is to assemble production-grade solutions turnkey.
Those who value a strong environment: if you want to learn in an active community and grow rapidly by sharing experiences.
Key features of the course
Step-by-step practical track: minimum theory, maximum work in the terminal.
Clear explanations: complex AI architectures explained in simple terms.
Debugging real workflows: focused on error handling and maintaining production code.
Starter code: ready-to-run projects.
5-pillar methodology: a step-by-step approach to creating autonomous agents.
Course program
Session 1: Fundamentals of Claude Code
May 28, 2026 | 5:00 PM – 8:00 PM (PT) | Live webinar
Basics of Claude Code
Agent cycle: data collection — action — verification.
Installation, first interactive session, slash commands, and hotkeys.
Access system and permission modes: Default, acceptEdits, plan, dontAsk, bypassPermissions.
Context Engineering
Context window as working memory. The "fresh and condensed" principle.
Context management commands.
Memory layers: CLAUDE.md, Second Brain, auto-memory, lazy loading.
Automation with Skills
Skills as reusable workflows. Anatomy of SKILL.md.
Custom skills, skill composition.
Independent practice:
Onboarding in new code: repository audit, architectural guide, onboarding guide.
Verification: initialization of CLAUDE.md and migration of legacy code.
Creating a skill: log triage, API integration, pipeline assembly /triage-logs.
Session 2: Scaling — MCP, Parallel Agents, and Agentic Engineering
May 29, 2026 | 5:00 PM – 8:00 PM (PT) | Live webinar
MCP, CLI, and agent toolkit
Model Context Protocol and its setup.
When MCP should not be used.
Browser automation via /chrome: navigation, screenshots, GIFs.
Self-correcting cycle: assembly — verification — fixes — verification.
Lifecycle of hooks.
Parallel development and validation
Git worktrees for safe parallel streams.
Multi-agent workflows and distributed work of multiple Claudes.
Sub-agents vs. agent teams.
Notification hooks.
Visual regression testing via /chrome.
5 pillars of Agentic Engineering
Typical scaling issues: context, skills, review.
Compound engineering and codebase audit.
Independent practice:
Integration of Claude via MCP: Figma, Blender, Slack, Notion.
Parallel assembly of a Full‑Stack application: independent worktrees, single API contract.
Diploma project: own solution or PRD:

File Name:  
Procoderr - Complete Backend with Node.js
Content Source:  
https://app.procodrr.com/web/checkout/66c86939c0a286ccc32c0d8b
Genre / Category:
Courses for Developers
Language:
HINDI
Original Price:  
₹9,999

Description:
Are you ready to master backend development and build powerful, scalable applications? The Complete Backend with Node.js course is designed to equip you with the skills you need—whether you want to develop high-performance applications or advance your career.
What you'll get?
➡️ Comprehensive Learning: Engage in over 200+ hours of planned content, covering Node.js fundamentals and advanced concepts, as well as Express.js, MongoDB, Redis, Docker, and more.
➡️ Real-World Projects: Apply your knowledge with 3 hands-on projects, including Google Drive Clone and ProCodrr Web App—a fully functional learning management system.
➡️ Core Techniques: Master command-line tools, OS fundamentals, networking, DBMS, RESTful APIs, MVC architecture, real-time communication, and more.
➡️ Advanced Topics: Dive into deeper concepts like clustering, compression, multithreading, cryptography, and WebSockets to enhance your applications.

File Name:  
Harkirat Singh - AI and ML Bootcamp
Content Source:  
https://100xdevs.com/new-courses/ai-and-ml-bootcamp
Genre / Category:
Courses for Developers
Language:
ENGLISH
Original Price:  
₹3,999

File Name:  
Senior Level Frontend Interview Prep
Content Source:  
https://master.dev/courses/interviewing-frontend-v2/
Genre / Category:
Courses for Developers
Language:
ENGLISH
Original Price:  
$399

Description:
Interviews are challenging. Throughout my career, I've gone through them many times, with both success and failure, and the best way to prepare is through practice. This intensive two-day workshop will help you get ready for interviews for frontend engineer positions at leading tech companies.Together, we will go over tasks of gradually increasing complexity—ranging from quick warm-up exercises to complex and non-trivial cases. You will cover key topics: fundamentals of JavaScript, component patterns in React, working with the DOM API, and advanced TypeScript capabilities. The tasks will be solved both in plain JavaScript and using React. Moreover, the difficulty level of the tasks is intentionally higher than in real interviews so that you feel confident in real conditions.During the workshop, you will learn to solve the most common interview tasks, master modern approaches and techniques for effectively addressing UI challenges, enhance your skills in creating components using Vanilla JS/TS and React, understand best practices for working with the DOM and event handling, and test your abilities with complex type-level tasks in TypeScript. Special attention is dedicated to the architecture of complex interfaces—such as tables, infinite canvases, and chats.The course is suitable for frontend developers of any level—from beginners to experienced professionals (Junior–Staff) who want to improve their interview skills and feel confident in interviews at top companies.

File Name:  
Vizuara AI Labs - AI Agents 10 Day Bootcamp
Content Source:  
https://vizuara.ai/courses/course_20005503
Genre / Category:
Courses for Developers
Language:
ENGLISH
Original Price:  
₹9,999

Description:
10-Day AI Agents Bootcamp — is a hands-on intensive course focused on creating AI agents, LLM applications, and automated AI systems. The course helps transition from understanding the principles of language models to developing real-world solutions: from simple agents to multi-agent systems, integrations with n8n, product packaging, and deployment in a production environment.
About the AI Agents 10 Day Bootcamp
AI Agents 10 Day Bootcamp is practice-oriented: instead of lengthy theory, participants sequentially create functional solutions based on artificial intelligence. During the training, you'll understand how AI agents differ from regular LLMs, how they use tools, memory, reasoning chains, and external services to perform complex tasks.
Throughout the bootcamp, you'll create 5+ ready-to-use AI agent systems, master popular frameworks for developing agent-based applications, and learn to design solutions that can be used in business, process automation, and the creation of AI products.
What You Will Learn
understand the structure of LLM, LLM agents, and agent-based AI systems;
distinguish AI agents from regular language models and chatbots;
design AI agent architecture for real-world tasks;
work with frameworks Smolagents, LangChain, and LangGraph;
create agents with tools, memory, reasoning logic, and state management;
develop multi-agent systems and configure interactions between agents;
use CrewAI to create teams of specialized AI agents;
integrate AI agents with automations, APIs, and external services via n8n;
create AI automation products and solutions for business processes;
test, evaluate, and improve the quality of AI agents;
deploy and scale LLM and AI agents using Kubernetes;
develop go-to-market strategies, determine pricing, and sell AI products.
10-Day Bootcamp Program
Day 1 — Introduction to LLM and LLM Agents
You will study the fundamental components of large language models, the principles of their operation, and key differences between LLMs, chatbots, and AI agents. This module forms the foundation for further agent system development.
Day 2 — Frameworks for AI Agents
Analysis of popular frameworks for creating AI agents: their capabilities, limitations, advantages, and application scenarios. You'll understand which tool to choose for a specific task.
Day 3 — Creating Agents with Smolagents
Practical development of lightweight and efficient AI agents based on Smolagents. You will create your first solution from idea to working prototype and consolidate basic principles of agent development.
Day 4 — Creating Agents with LangChain
Working with components of LangChain: tools, memory, chains, prompts, and advanced reasoning mechanisms. This module helps create more flexible and functional LLM applications.
Day 5 — Creating Agents with LangGraph
Developing complex agent processes using LangGraph. You will study state management, multi-step task execution, conditional logic, and constructing more reliable AI scenarios.
Day 6 — Multi-Agent Systems
Creation of systems where multiple AI agents jointly solve complex tasks. You'll explore principles of coordination, role distribution, communication, and quality control between agents.
Day 7 — CrewAI
Practice with CrewAI: creating teams of AI agents with different roles, responsibilities, and goals. This approach is suitable for automating research, content generation, data analysis, and business processes.
Day 8 — n8n and Automation
Integration of AI agents with n8n, external services, and APIs. You will learn to build automated workflows that connect AI logic with real tools without needing to write a large amount of code.
Day 9 — Creating AI Agent-based Products
Applying acquired knowledge to design real AI automation products. You will explore how to find tasks for automation, package solutions, and turn agent systems into useful products.
Day 10 — Selling AI Automation Products
Market entry strategies, positioning, pricing, marketing, and monetization of AI products. This day helps link technical development with commercial application of solutions.
Practical Learning Format
The bootcamp is structured as intensive learning with live coding, workshops, and practical assignments. Participants not only learn tools for developing AI agents but also apply them to create their own projects and automations.
Class recordings allow material to be absorbed at a convenient pace, revisit challenging topics, and repeat practical demonstrations. This format is suitable for those who want to quickly master AI agent development and immediately apply the knowledge in their work.
Who This Course is For
developers who want to create LLM applications and AI agents;
AI engineers and ML specialists looking to master agent frameworks;
no-code and low-code specialists working with automation via n8n;
entrepreneurs and product managers who want to launch AI automation products;
business process automation specialists;
those who want to understand the practical application of LLM, LangChain, LangGraph, CrewAI, and Kubernetes.
What You Will Gain After Completing the Bootcamp
understanding of the architecture of modern AI agents and multi-agent systems;
practical experience with key frameworks for agent development;
a portfolio of several AI systems and automations;
skills in integrating AI agents with external services and APIs;
understanding approaches to testing, evaluating, and improving AI solutions;
a foundation for creating, launching, and selling your AI automation products.
Why Learn AI Agents Now
AI agents are becoming one of the key directions in the development of artificial intelligence. They can not only generate responses but also perform tasks, use tools, analyze data, interact with services, and automate complex processes.
10-Day AI Agents Bootcamp helps swiftly master in-demand technologies and gain practical skills that can be applied in development, automation, consulting, startup creation, and launching commercial AI products.

File Name:  
Namaste AI - Learn AI, LLMs, RAG & AI Agents from Scratch 🔥
Content Source:  
https://namastedev.com/learn/namaste-ai
Genre / Category:
Courses for Developers
Language:
ENGLISH
Original Price:  
₹8,564

Description:
Understand how LLMs and ChatGPT work
Study transformers, tokenization, and hallucinations
Become an AI-native software engineer
Use AI across the complete software development lifecycle
Master prompting techniques, including zero-shot prompting
Build AI-powered products using existing LLM APIs
Learn API integration, tokens, and API protection
Build RAG systems using private or business data
Learn AI agents and Model Context Protocol
One purchase includes all five planned seasons
THE FOUNDATION
Build a strong understanding of how modern AI systems, LLMs and Generative AI actually work
Learn the essential concepts behind transformers, tokens, embeddings, context, prompting and hallucinations without unnecessary academic complexity.

File Name:  
ROBOTICS 101 - Master the First Principles of Robotics
Content Source:  
https://www.ubicoders.com/foundations/robotics/robotics101
Genre / Category:
Courses for Developers
Language:
ENGLISH
Original Price:  
$249

Description:
Stop copying code you don't understand. In this course, you will build a fully autonomous drone and ground robot from scratch, relying on the fundamental principles of robotics: control theory, system dynamics, and sensor fusion. No ROS, no "magic" libraries or ready-made solutions—just engineering thinking and working from first principles.
The training is built on a strict engineering cycle Math → Code → Reality. First, you will delve into the mathematics and physics that underpin the algorithms. Then you will translate these equations into code. Finally, you will test the result on virtual robots operating in conditions with noisy and imperfect signals that are close to real-life scenarios.
Instead of using "black boxes," you will independently implement drivers, controllers, and state estimation algorithms, including filtering and sensor data fusion. You will learn to understand the system at the level of models and matrices, rather than at the level of ready-made APIs and libraries.
The course changes the approach to robotics: from the role of an integrator who assembles others' solutions to the role of an engineer-architect who understands how a machine perceives and describes reality through mathematics and code. As a result, you will gain confidence in designing autonomous systems and be able to explain their operation—from physical models to control and motion planning algorithms.
This course is for those who want to go beyond tutorials and become engineers capable of independently designing and developing complex robotic systems.

File Name:  
NamasteSQL - Power BI : Go from Zero to Hero (Self Paced) by Ankit Bansal
Content Source:  
https://www.namastesql.com/course-detail/power-bi-go-from-zero-to-hero-self-paced
Genre / Category:
Courses for Developers
Language:
ENGLISH
Original Price:  
₹2,999

Description:
Are you ready to transform raw data into actionable insights and stunning visualizations? Join our Power BI Bootcamp and master the tools and techniques to become a data analysis pro!

In this hands-on, interactive bootcamp, you’ll learn how to:

Connect, clean, and transform data with Power Query
Learn dimensional modeling to build efficient data model
Use DAX (Data Analysis Expressions) for powerful calculations and analytics
Build dynamic, interactive dashboards and reports
Share and collaborate on insights with your team using Power BI service

Whether you’re a beginner or looking to level up your skills, our expert-led sessions will equip you with the knowledge and confidence to harness the full potential of Power BI.
Course Highlights
10+ in-depth sessions (2 hours each) with lifetime access to recordings.
Lifetime access to premium Namaste SQL community to discuss questions and doubts.
Entire bootcamp by Ankit Bansal

File Name:  
Maven - Systematically Improving RAG Applications + (Bonus Content)
Content Source:  
https://maven.com/applied-llms/rag-playbook
Genre / Category:
Courses for Developers
Language:
ENGLISH
Original Price:  
$2,199

Description:
Stop building RAG systems that impress in demos but disappoint in production
Transform your retrieval from “good enough” to “mission-critical” in weeks, not months. Most RAG systems stall in prototype purgatory: they demo well, but fail on complex queries—eroding trust and wasting engineering time. The difference isn’t just better tech, but a systematic mindset.
With the RAG Flywheel, you’ll:
✅ Pinpoint failures with synthetic evals
✅ Fine-tune embeddings for 20–40% gains
✅ Collect 5x more user feedback
✅ Segment queries to target high-impact fixes
✅ Build multimodal indices for docs, tables, images
✅ Route queries to the best retriever automatically
Week by week, you move from vague “make it better” to clear metrics, focused improvements, and compounding value. Real-world results include +20% accuracy from re-ranking, +14% with cross-encoders, and $50M revenue boosts from better search.
Join 400+ engineers applying this framework in production. Instructor Jason Liu has built multimodal retrieval and recommendation systems at Facebook, Stitch Fix, and through consulting—experience that shaped this practical, battle-tested approach.
What you’ll learn
Follow a repeatable process to continually evaluate and improve your RAG application
Analyze and Diagnose RAG System Performance
Construct Data-Driven Improvement Frameworks
Design Specialized Search Systems
Optimize Query Understanding & Routing

File Name:  
Ultimate Go: Software Design with Kubernetes 2.0
Content Source:  
https://www.ardanlabs.com/training/individual-on-demand/ultimate-go-bundle/
Genre / Category:
Courses for Developers
Language:
ENGLISH
Original Price:  
$699
 
Description:
From the beginning of the course, you will pair-program with your instructor Bill Kennedy as he walks you through the design philosophies, architectural decisions, and best practices as they apply to engineering a production-ready Go service.
With each new feature that is added to the service, you will learn how to deploy and manage the Kubernetes environment used to run the service. Throughout the class, the code being worked on is pushed to a repository for personal access and review.
Requirements:
You need to know how to program and core concepts of working with computers (such as files, HTTP, memory management…)
Studied CS in school or has a minimum of two years of experience programming full time professionally.
Familiar with structural and object-oriented programming styles.
Has worked with arrays, lists, queues and stacks.
Understands processes, threads and synchronization at a high level.
Operating Systems:
Has worked with a command shell.
Knows how to maneuver around the file system.
Understands what environment variables are.
You should have the following installed on your computer prior to the workshop:
Go SDK from golang.org/dl/
An IDE, either VSCode with Go extension or GoLand
Git

File Name:  
Maven - Production-Ready Systems with LLMs and Agents: An Intensive for Engineers
Content Source:  
https://maven.com/gazar/production-ready-systems-with-llms-and-agents-an-intensive-for-engineers
Genre / Category:
Courses for Developers
Language:
ENGLISH
Original Price:  
$1,500
 
Description:
Learn to create LLM and agent systems that withstand real traffic, cost constraints, and inevitable failures.
Almost any engineer can connect an LLM and assemble an impressive demo today. But upon moving to production, all the things that are usually not taught become apparent: costs start to rise, latency increases, the model confidently provides incorrect answers to the user, and without evaluations, you can't timely detect quality degradation and without tracing, you can't understand what exactly went wrong.
The demo is the easiest part.
If you are responsible for implementing AI features in production, you have to bridge this gap yourself: experiment, rely on tutorials that usually end on the happy path, and hope that the system will withstand real-world operation.
This intensive focuses on that engineering part which most educational materials skip: architectural solutions that allow LLM and agent systems to work under real loads, fit within the set budget, and handle failures correctly.
You will understand where the boundary should lie between code and model, how to control cost and latency, how to design systems considering the non-determinism of LLM, how to build evaluations and observability, and where human oversight is necessary.
As a result, you will have not just another demo, but production-ready artifacts that can be used in work as soon as next week: an architectural document of an AI agent, a cost and latency budget, an eval harness plan, and a runbook for handling failures.
What You Will Learn
Correctly Divide Responsibility Between Code and Model
Determine which tasks to assign to the LLM and which to leave in deterministic code—one of the most important architectural decisions in agent systems.
Identify tasks where the model adds more risk than benefit and replace it with conventional software logic.
Design prompts and tool interfaces as narrow and understandable contracts, so that the model's tasks remain limited and testable.
Control Cost and Latency Under Real Traffic
Set a cost and response time budget for each request and design the system with these constraints in mind.
Use caching, batching, and routing between models to reduce expenses without significant loss of quality.
Select the model for the specific task instead of using the most powerful model for everything.
Design the System Considering Non-Determinism and Failures
Implement retries, timeouts, and fallbacks so that a slow or unavailable model does not block the entire system.
Create graceful degradation for cases when the model makes errors, is unsure of the answer, or is completely unavailable.
Limit the influence of non-determinism with validation and guardrails before the result reaches the user.
Choose the Right Architecture for the AI Agent
Understand when to use tool calling, planning loops, memory, or multiple interacting agents.
Recognize situations when a well-designed single agent is more effective than a complex multi-agent architecture.
Link each architectural pattern to its potential failure modes and make decisions based on engineering trade-offs rather than hype.
Detect Quality Degradation with Evaluations
Create an eval harness that allows evaluating changes before they go to production, rather than after user complaints.
Combine offline test sets with online signals to detect drift and subtle quality drops.
Turn subjective "it seems worse" into measurable quality gates within the CI/CD process.
Confidently Operate Agent Systems
Trace each LLM call to understand what the system actually did, rather than assume what it should have done.
Monitor cost, latency, and quality through metrics and dashboards that allow early problem detection.
Determine where human oversight and prompt versioning truly mitigate risks.
Who This Course Is For
Product and Backend Engineers tasked with implementing LLM features and who are capable of creating AI demos but have not yet deployed such systems in production.
Senior and Staff Engineers responsible for AI systems in production and aiming to establish a systematic approach to architecture, cost, resilience, and quality.
Tech Leads and Architects who need to evaluate different agent patterns and choose solutions that can scale and operate stably under load.
This course is not about creating another impressive AI demo. It's about turning LLMs and AI agents into engineering systems that can be trusted in production.

File Name:  
Hello Interview - Learn Behavioral
Content Source:  
https://www.hellointerview.com/learn/behavioral
Genre / Category:
Courses for Developers
Language:
ENGLISH
Original Price:  
$5,599
 
Description:
Learn Behavioral — a practical course for preparing for behavioral interviews in technology companies. You will learn to understand what exactly the interviewer is evaluating, choose the right examples from your experience, and convincingly talk about your achievements, leadership, decision-making, and interactions with people.
The course is based on the experience of conducting over 1,000 behavioral interviews in large technology companies and coaching over 200 candidates. Instead of universal advice like "just use STAR," the program shows how interviewers actually assess candidates and what signals they look for in their responses.
What You Will Learn
You will understand how a behavioral interview is structured and what competencies affect the hiring decision. The core of the course is the Decode → Select → Deliver cycle, which helps systematically answer even unexpected questions.
Decode — understand what the interviewer really wants to know from the given question.
Select — choose the most appropriate story from your experience considering its scale, relevance, uniqueness, and recency.
Deliver — tell the story in a structured and convincing way using the CARL: Context, Actions, Results, Learnings framework.
You will also learn to create your own catalog of professional stories and find examples in your experience that demonstrate the necessary competencies.
What is Evaluated in a Behavioral Interview
The course explains three key dimensions used in evaluating candidates:
Signal Areas — eight main competencies, including Ownership, Perseverance, and Conflict Resolution.
Company Values — the specific values of the company and the qualities it expects from employees.
Cultural Assessment — the characteristics of the corporate culture and the accepted ways of interaction and decision-making within the company.
You will learn to connect interview questions with specific competencies and adapt your answers to meet the company's requirements and culture.
Preparing for the Most Important Questions
Special attention is given to three questions that are particularly common in behavioral interviews:
"Tell me about yourself."
"Tell me about your favorite project."
"Tell me about a conflict."
You will learn how to prepare strong answers, not by memorizing a script, but by using your own professional experience.
Preparing for Interviews in Big Tech
The course examines the characteristics of behavioral interviews in companies like Meta, Amazon, and Google. You will learn how to adjust the wording, emphasis, and delivery of your stories based on the expectations of a specific company.
Special importance is given to approaches for interviews for senior positions, where not only technical knowledge is assessed, but also the ability to independently solve business problems, work with complex teams, build trust, make decisions, and create influence beyond one's immediate team.
Who This Course is For
The course is suitable for developers, engineers, managers, and other specialists who are facing a behavioral interview, especially when applying to large technology companies.
It will be useful both for candidates facing a behavioral interview for the first time and for those who have already undergone technical interviews but were rejected specifically at the behavioral stage.
The main goal of the course is to teach you not just to talk about your experience, but to show through it how you think, make decisions, interact with people, and create results.