AI meets MLOps - Making sense of the mess

In this episode of AI x DevOps, Rohit sits down with Görkem Ercan, CTO at Jozu, a company building a DevOps platform for AI agents and models. Görkem, a veteran with over two decades of software experience (including contributions to the Eclipse Foundation), explains why MLOps is fundamentally different from traditional, deterministic DevOps—leading to extreme pipeline fragmentation.

Standardization is Key

Discover why OCI is the recognized standard for packaging AI/ML artifacts, and how the Model Packs project (with ByteDance, Red Hat, and Docker) is defining the artifact structure. Learn how standardization is bringing order to the fragmented MLOps landscape.

Open Source Challenges

Understand the critical challenges maintainers face when receiving large amounts of untested, verbose, AI-generated code. Görkem shares insights on the impact of AI-generated Pull Requests on open-source projects.

LLM Economics and Strategy

Explore why running small, fine-tuned LLMs in-house can be cheaper and provide more predictable, consistent results than generic large providers. Get practical insights on when to build versus buy.

KitOps Solution

Learn how KitOps creates an abstraction that allows data scientists to focus on training while leveraging existing DevOps platforms for deployment. Discover how ModelKits are simplifying the AI/ML deployment pipeline.

Essential listening for platform engineers, DevOps practitioners, MLOps engineers, and anyone working at the intersection of AI and infrastructure. Tune in to understand the standardization movement reshaping the future of AI development.