MLOps is a practice for collaboration between Data Science and operations to manage the production machine learning (ML) lifecycles. As an amalgamation of “machine learning” and “operations,” MLOps applies DevOps principles to ML delivery, enabling the delivery of ML-based innovation at scale to result in:

• Faster time to market of ML-based solutions
• More rapid rate of experimentation, driving innovation
• Assurance of quality, trustworthiness, and ethical AI

MLOps is essential for scaling ML. Without it, enterprises risk struggling with costly overhead and stalled progress. Several vendors have emerged with offerings to support MLOps: the major offerings are Microsoft Azure ML and Google Vertex AI. We looked at these offerings from the perspective of enterprise features and time-to-value.

This session will be informative and helpful in uncovering some of the challenges and nuances of MLOps program development and platform selection.