Explainability in the MLOps Cycle - MLOps Podcast Episode 138

Explore the intricacies of explainability in the MLOps cycle with Dattaraj Rao in this 46-minute podcast episode. Gain insights into Rao's top three areas of interest in Machine Learning, emerging trends, and his company's focus areas. Discover the similarities between deploying rule-based systems and ML models, and learn about incorporating various systems. Delve into Rao's extensive experience in AI/ML, including his work at Persistent's AI Research Lab and his 19-year tenure at General Electric. Understand the applications of Knowledge Graphs, NLU, Responsible AI, and MLOps in Healthcare, Banking, and Industrial domains. Explore topics such as Large Language Models, Remote Monitoring and Diagnostics, and key Machine Learning Design Patterns. Get a glimpse into Rao's book "Keras to Kubernetes: The Journey of a Machine Learning Model to Production" and his thoughts on model registries and multi-tenancy.

[] Dattaraj's preferred coffee
[] Introduction to Dattaraj Rao
[] Takeaways
[] This podcast is brought to you by Superwise!
[] Dattaraj's background
[] Top 3 interests of Dattaraj
[] Examples of Large Language Models use cases not as a good application
[] Future of Large Language Models - change or inherent problem
[] Remote Monitoring and Diagnostic
[] Keras to Kubernetes book
[] Dattaraj's title to his next book
[] Machine Learning Design Patterns to keep in mind
[] Model registries and multi-tenancy
[] Wrap up