Scaling MLOps with Self-Service Platforms

Explore the evolution and implementation of self-service platforms for scaling machine learning operations in this 11-minute conference talk from Conf42 MLOps 2025. Learn about the critical importance of self-service platforms in modern ML operations and examine comprehensive platform architecture designs that enable scalable MLOps implementations. Discover the key components essential for building effective MLOps platforms, including governance layers that ensure compliance and control across ML workflows. Understand how to optimize developer experience through self-service capabilities that empower teams to independently manage their ML pipelines. Navigate the strategic build versus buy decision-making process for MLOps tooling and infrastructure. Gain insights into emerging future trends shaping the MLOps landscape and their potential impact on organizational ML strategies. The presentation covers the complete journey from understanding ML operations evolution to implementing practical solutions for scaling MLOps through platform-driven approaches.