courseraПрограммирование

RAG Systems and Production Operations

Курс от Coursera

Средний≈ 9.5 чАнглийский

О курсе

This advanced course transforms you into an enterprise-level ML engineer capable of designing, implementing, and operating sophisticated retrieval-augmented generation (RAG) systems. You'll progress from foundational RAG architecture to cutting-edge patterns like Self-RAG and Corrective RAG, then dive deep into production operations including secure deployment, performance optimization, and cross-platform migration. By combining hands-on projects with real-world enterprise requirements, you'll learn to build AI systems that deliver accurate, grounded responses at scale. Each module builds practical skills used by senior ML engineers in high-stakes domains like legal tech, healthcare, and finance. Who this is for: Experienced software engineers and data scientists ready to build production-grade AI applications. Strong Python programming and basic machine learning knowledge required.

Навыки, которые вы освоите

Retrieval-Augmented Generation Performance Tuning Vector Databases Data Migration Embeddings Model Deployment AI Security System Monitoring Role-Based Access Control (RBAC)Agentic systems Generative AI Agents Generative AI Containerization Large Language Modeling Model Optimization

Программа курса

5 модулей · 42 учебных материалов

01Understand RAG Basics10 материалов

AI Coach: "My LLM is Hallucinating!"DIALOGUE The Components of a RAG System: Retriever and GeneratorЧтение How-To: Diagram the RAG Data FlowВидео Hands-On Learning: Sketch a RAG Architecture DiagramЗадание

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