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Keerti Purswani - Agentic AI System Design 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 Information Submitter S A N Submitted 10/11/2026 Category Paid Coding Courses Sale page https://educosys.com/course/agentic-ai-system-design View File

Keerti Purswani - Hands-On Generative AI 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 LearnUnderstand 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 Program1. Fundamentals of Generative Artificial IntelligenceStart 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 ModelsExplore 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 LangGraphLearn 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 RAGMaster 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 AIGet 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 IntelligenceThe 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 OutcomeBy 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 Information Submitter S A N Submitted 10/10/2026 Category Paid Coding Courses Sale page https://educosys.com/course/hands-on-generative-ai View File

Keerti Purswani - HHLD - Hands-On High Level System Design Implementation 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 CourseThe 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 ForBackend 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 LearnDesigning 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 ProgramWeek 1 — Basics of AWS and Setting Up the EnvironmentIn 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 SystemIn 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 PlatformThe 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 SystemThe 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 SystemsThe 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 TrainingAfter 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 PracticeIn 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 Information Submitter S A N Submitted 10/10/2026 Category Paid Coding Courses Sale page https://educosys.com/course/hands-on-high-level-system-design-implementation View File

Build AI Agents that Never Sleep 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 CourseDuring 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 SystemThe 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 FailuresNext, 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 FailuresNext, 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 ExecutionSpecial 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 LearnUnderstand 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 Information Submitter S A N Submitted 10/03/2026 Category Paid Coding Courses Sale page https://master.dev/workshops/background-agents/ View File

Vizuara Ai Labs - Build a Small Language Model From Scratch File Name: Vizuara Ai Labs - Build a Small Language Model From Scratch Content Source: https://slm.vizuara.ai/ Genre / Category: Premium courses Password: Click Here to Reveal Password Original Price: ₹24,999 Language: ENGLISH ABOUT THE COURSE: "Build a Small Language Model From Scratch" is an intensive, hands-on 3-day workshop designed to help participants master the complete, end-to-end pipeline of developing production-ready Small Language Models (SLMs). Moving far beyond theoretical concepts, this bootcamp empowers learners to construct a fully functional language model from the ground up. By the end of the course, participants will have a deep, practical understanding of every critical component that powers modern AI systems, from initial dataset creation to final production deployment. Detailed Curriculum The course is meticulously structured into 8 comprehensive modules spread across 3 intensive days. Every module is reinforced with practical, hands-on coding exercises to ensure a deep, applied understanding of the material. Day 1: Foundation & Data Preparation The journey begins with the most crucial step in AI development: data. High-quality models require high-quality data. Module 01: Creating the Dataset: Learn industry best practices for data collection, rigorous cleaning, and advanced preprocessing techniques required to prepare high-quality training data for a language model. Module 02: Tokenizing the Dataset: Master various tokenization strategies, vocabulary building, and efficient text encoding methods that translate raw human text into a numerical format the model can process. Module 03: Creating Input-Target Pairs: Understand how to structure your processed data for supervised learning, ensuring proper sequence formatting for optimal model training and learning. Day 2: Architecture & Training This day focuses on the core engineering and mathematical foundations of the model itself. Module 04: SLM Architecture Design: Build a complete neural network architecture from scratch. This includes implementing embeddings, multiple neural layers, and advanced attention mechanisms (the backbone of modern transformer models). Module 05: Pre-training Setup: Learn to configure robust training loops, select appropriate loss functions, choose the right optimizers, and set up comprehensive performance monitoring systems to track progress. Module 06: Pre-training the SLM: Execute the actual training process, learning industry-standard practices for checkpointing, handling interruptions, and implementing validation strategies to ensure the model generalizes well and avoids overfitting. Day 3: Deployment & Production The final day bridges the gap between a trained model sitting in a notebook and a real-world, scalable application. Module 07: Inference & Evaluation: Implement efficient text generation, explore different sampling strategies (such as temperature and top-p tuning), and apply standard model evaluation metrics to objectively assess performance. Module 08: Production Acceleration: Discover how to optimize your trained model for deployment, scale it efficiently, and meet the stringent performance, memory, and latency requirements of real-world production environments. Key Benefits & Features Complete Development Framework: Participants will receive the entire SLM development framework, providing a reusable, battle-tested blueprint for any future AI projects. Live Interactive Sessions: The workshop features 3 days of intensive, hands-on, live sessions that include real-time Q&A and dedicated debugging support from the instructional team. Expert Guidance: Learn directly from industry professionals who have hands-on, proven experience building and scaling production-grade language models. File Information Submitter S A N Submitted 10/02/2026 Category Courses Sale page https://slm.vizuara.ai/ View File

Agentic AI Masters 2026: LangChain, LangGraph & CrewAI 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 Information Submitter S A N Submitted 09/29/2026 Category Paid Coding Courses Sale page https://www.udemy.com/course/agentic-ai-masters-langchain-langgraph-crewai-n8n/ View File

StudyGlows - Artificial Intelligence + Youtube (Change your life) File Name: StudyGlows - Artificial Intelligence + Youtube (Change your life) Content Source: https://fmatb.courses.store/735356 Genre / Category: Premium courses Password: Click Here to Reveal Password Original Price: ₹4,210 Language: HINDI File Information Submitter S A N Submitted 09/29/2026 Category Courses Sale page https://fmatb.courses.store/735356 View File

MLOps Bootcamp: Build Real-World AI Infrastructure 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 CourseTraining 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 PracticeDuring 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 LearnUnderstand 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 TechnologiesThe 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 StructuredFrom Experiment to Production-Ready ModelYou 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 RetrainingThe 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 VersionsA 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 ForML 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 KnowledgeFor 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 CompletionBy 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 Information Submitter S A N Submitted 09/29/2026 Category Paid Coding Courses Sale page https://zerotomastery.io/courses/learn-mlops-engineering/ View File