Skip to content
Artwork for Code, Cloud & AI
Code, Cloud & AI · Tuesday · 38 min

From Big Data to Agentic AI with Microsoft Foundry with Heena Refai

In this episode, I sat down with Heena Refai, a Cloud Solution Architect at Microsoft, to talk about how enterprise AI has evolved from the big-data and traditional machine learning era into today’s generative AI and agentic systems landscape. Heena reflected on her background in data engineering and machine learning, why strong data foundations still matter even in the age of large pretrained models, and why so many organizations are racing into AI without fully addressing the quality, governance, and structure of the data underneath it. We then dug deep into Microsoft Foundry: what it is, how the model catalog works, how models become agents through prompts, tools, and enterprise data, and how teams can build, host, evaluate, and govern AI systems at scale. Hina explained Foundry Agent Service, project-based organization, cost controls, Azure API Management as an AI gateway, and the growing need for centralized governance with Microsoft Agent 365. We also touched on developer productivity with GitHub Copilot and why, despite all the advances in AI, fundamentals still matter more than ever. Heena Refai on LinkedIn: Heena Refai | LinkedIn Microsoft Foundry: https://learn.microsoft.com/en-us/azure/foundry/ Microsoft Foundry Agent Service: https://learn.microsoft.com/en-us/azure/foundry/agents/overview Microsoft Agent Framework: https://learn.microsoft.com/en-us/agent-framework/ Microsoft Agent 365: https://learn.microsoft.com/en-us/office365/servicedescriptions/microsoft-agent-365/microsoft-agent-365 Azure API Management: https://azure.microsoft.com/en-us/products/api-management Azure Pricing Calculator: https://azure.microsoft.com/en-us/pricing/calculator/ Azure Container Apps: https://learn.microsoft.com/en-us/azure/container-apps/overview Azure AI Search: https://learn.microsoft.com/en-us/azure/search/search-what-is-azure-search Azure Cosmos DB: https://learn.microsoft.com/en-us/cosmos-db/ Azure OpenAI Service: https://azure.microsoft.com/en-us/pricing/details/azure-openai/ Anthropic: https://www.anthropic.com/company Claude models in Microsoft Foundry: https://learn.microsoft.com/en-us/azure/foundry/foundry-models/concepts/claude-models Claude Fable 5 in Microsoft Foundry Models: https://ai.azure.com/catalog/models/claude-fable-5 Hugging Face: https://huggingface.co/ IBM: https://www.ibm.com/us-en MongoDB: https://www.mongodb.com/products Databricks: https://www.databricks.com/ SAP: https://www.sap.com/ ServiceNow: https://www.servicenow.com/ Microsoft Teams: https://www.microsoft.com/en-us/microsoft-teams/teams-products Slack: https://slack.com/ GitHub Copilot: https://docs.github.com/en/copilot/get-started Visual Studio Code: https://code.visualstudio.com/ AWS: https://aws.amazon.com/what-is-aws/ Kubernetes: https://kubernetes.io/

0:00 · Hina's background in AI and machine learning-38:26

transcript

No transcript — this publisher did not publish one.

show notes

In this episode, I sat down with Heena Refai, a Cloud Solution Architect at Microsoft, to talk about how enterprise AI has evolved from the big-data and traditional machine learning era into today’s generative AI and agentic systems landscape. Heena reflected on her background in data engineering and machine learning, why strong data foundations still matter even in the age of large pretrained models, and why so many organizations are racing into AI without fully addressing the quality, governance, and structure of the data underneath it.

We then dug deep into Microsoft Foundry: what it is, how the model catalog works, how models become agents through prompts, tools, and enterprise data, and how teams can build, host, evaluate, and govern AI systems at scale. Hina explained Foundry Agent Service, project-based organization, cost controls, Azure API Management as an AI gateway, and the growing need for centralized governance with Microsoft Agent 365. We also touched on developer productivity with GitHub Copilot and why, despite all the advances in AI, fundamentals still matter more than ever.

links26

chapters

12 chapters