Managing data at scale is one of the biggest challenges in modern cloud architecture. As data volumes grow from terabytes to petabytes, manual operations become impossible, and simple scripts become brittle and insecure. Azure Storage Actions, a serverless framework for automating data management in Azure Storage, offers a powerful solution. It provides a no-code, scalable, and secure way to handle operations like lifecycle management, data organization, and policy enforcement across billions of objects.

Discover how Microsoft’s two key frameworks, Semantic Kernel and AutoGen, provide a powerful stack for creating sophisticated multi-agent AI systems. This article explores how to build specialized agents with Semantic Kernel and orchestrate their collaboration with AutoGen, while also comparing this approach to leading alternatives like CrewAI and LangGraph.

I recently finished reading “Unlocking Data with Generative AI and RAG” by Keith Bourne, and it provides a comprehensive overview of building systems that can leverage large language models (LLMs) with your own private or new data. This post summarizes my key takeaways from the book.

The core idea is that the key technical components of a Retrieval-Augmented Generation (RAG) system include the embedding model that creates your embeddings, the vector store, the vector search mechanism, and the LLM itself.