AI systems often face challenges with performance, scalability, and maintainability due to monolithic designs. In this course, Modular RAGs, you’ll learn to implement modular architectures for RAG models. First, you’ll explore techniques for decomposing RAG systems into independent modules, such as retrieval, encoding, and generation. Next, you’ll discover how to implement flexible and reusable designs for improved system performance. Finally, you’ll learn how to evaluate the benefits and trade-offs of modular RAG designs in terms of performance, scalability, and maintainability. When you’re finished with this course, you’ll have the skills and knowledge of modular RAG architectures needed to optimize complex AI systems effectively.
Author Name: JS Padoan
Author Description:
With 20 years of experience in the Microsoft eco-system, Jean-Sébastien assists clients in designing, building, developing and deploying Microsoft solutions in on-premise or Azure / Office365 environments. JS has skills, experience and certifications in Windows Server / .NET / SharePoint / SqlServer / Office365 / PowerPlatform. He also teaches these subjects as a Microsoft certified trainer for 13 years. He is also a Microsoft power-seller in the area of data and analysis and is therefore able t… more

Table of Contents

• Implementing Modular Architectures for RAG Systems
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