Explore the current state and future of MLOps in this insightful podcast episode featuring Patrick Barker, CTO of Kentauros AI, and Farhood Etaati, MLOps/Platform Team Lead at AIMedic. Delve into the challenges and opportunities presented by the rise of Large Language Models (LLMs) and the emergence of "LLMLOps." Examine the similarities between new challenges and those faced by older-generation ML models in production. Discuss the value of specialized tools, the skepticism surrounding tool adoption, and the role of the MLOps community in addressing these issues. Investigate the reasons behind open-source MLOps lagging and the impact of venture capital funding. Gain valuable insights on topics such as MLOps vs. DevOps challenges, the relationship between MLOps and data engineering, and the advancements in ML 2.0. Explore the use of generative AI in MLOps and the ongoing challenges of ML reproducibility. Perfect for professionals and enthusiasts interested in the evolving landscape of machine learning operations and its intersection with cutting-edge AI technologies.
MLOps at the Crossroads - Challenges and Future Directions
Overview
Syllabus
[] Farhood's and Patrick's preferred coffee
[] Takeaways
[] Please like, share, and subscribe to our MLOps channels!
[] Strong feelings
[] MLOps vs DevOps Challenges
[] Medical setting, ML tools, NLP, model building
[] MLOps vs Data Engineering
[] MLOps Boosts LLM Development
[] Longtail Use Cases
[] Tech Roles Distinctions
[] Did He Say That?
[] Fine-tuning AI Models
[] ML 2.0 Advancements Explained
[] Generative AI in MLOps
[] ML Reproducibility Challenges
[] Wrap up
Taught by
MLOps.community
