Raj Rikhy is a Senior Product Manager at Microsoft AI + R, enabling deep reinforcement learning use cases for autonomous systems. Previously, Raj was the Group Technical Product Manager in the CDO for Data Science and Deep Learning at IBM. Prior to joining IBM, Raj has been working in product management for several years - at Bitnami, Appdirect, and Salesforce.

// MLOps Podcast #268 with Raj Rikhy, Principal Product Manager at Microsoft.

// Abstract

In this MLOps Community podcast, Demetrios chats with Raj Rikhy, Principal Product Manager at Microsoft, about deploying AI agents in production. They discuss starting with simple tools, setting clear success criteria, and deploying agents in controlled environments for better scaling. Raj highlights real-time uses like fraud detection and optimizing inference costs with LLMs, while stressing human oversight during early deployment to manage LLM randomness. The episode offers practical advice on deploying AI agents thoughtfully and efficiently, avoiding over-engineering, and integrating AI into everyday applications.

// Bio

Raj is a Senior Product Manager at Microsoft AI + R, enabling deep reinforcement learning use cases for autonomous systems. Previously, Raj was the Group Technical Product Manager in the CDO for Data Science and Deep Learning at IBM. Prior to joining IBM, Raj has been working in product management for several years - at Bitnami, Appdirect, and Salesforce.

// MLOps Swag/Merch

https://mlops-community.myshopify.com/

// Related Links

Website: https://www.microsoft.com/en-us/research/focus-area/ai-and-microsoft-research/

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Timestamps:

[00:00] Raj's preferred coffee

[00:16] Takeaways

[00:23] Join the AI Agents in Production Conference on November 13th!

[01:25] Categorizing different agents

[06:59] Agent environment frameworks

[15:52] Debugging Strategies for Complex Systems

[22:26] Evaluating Agent Frameworks Effectively

[28:30] Defining success in projects

[31:45] Process simplification benefits

[35:32] Agent workflow use cases

[39:29] Tinder for clothing recommendation

[44:20] Speed Reliability Trade-offs in ML

[48:06] Brilliant minds and doubts

[48:50] Wrap up