Are you ready to take your enterprise AI applications from prototype to production? Join us as we explore the power of Microsoft AI Foundry with expert Edgar McOchieng [MVP] to learn how to build scalable, secure, and cost-effective AI agents. Whether you are managing skyrocketing API costs or looking to ground your models in enterprise data, this episode provides a professional roadmap for success.

In this deep dive, Edgar McOchieng, a Microsoft MVP and Data/AI Engineer, shares his extensive experience building Mela AI and mentoring developers through Ujuzi Hub in Kenya. We cover everything from the fundamentals of AI Foundry to advanced topics like hybrid model orchestration and Retrieval Augmented Generation (RAG). You will learn why governance and monitoring are the most critical pillars for any enterprise AI strategy and how to choose between Large Language Models (LLMs) and Small Language Models (SLMs).

Key topics discussed in this episode:
⚡ Managing AI costs using local models and rate limiting
⚡ The difference between Copilot Studio and AI Foundry
⚡ How to use Prompt Flow and Hosted Agents for custom builds
⚡ Implementing RAG with vector search and semantic indexing
⚡ Security strategies for multi-tenant enterprise applications
⚡ The future of prompt engineering and model orchestration

Chapters:
0:00 Intro and Guest Introduction
3:15 Community Building and Ujuzi Hub in Kenya
7:40 Experience with Azure and AI Services
12:20 Building Mela AI: An Enterprise Case Study
17:15 Managing AI Costs with Hybrid Models
22:30 What is Microsoft AI Foundry?
27:00 Comparing Copilot Studio vs AI Foundry
32:10 Core Components and Prompt Flow Explained
36:45 Navigating the Model Catalog and Marketplace
41:20 Evaluation Pipelines and Hallucination Testing
45:50 RAG Architecture and Vector Search
51:00 Enterprise Security and Multi-Region Scaling
56:15 The Future of SLMs and Prompt Engineering
1:00:10 Final Priorities for AI Transformation

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