MLOps vs ModelOps - What's the Difference and Why You Should Care

Explore the distinctions between MLOps and ModelOps in this 32-minute conference talk by Jim Olsen, CTO at ModelOps, presented at the Toronto Machine Learning Series (TMLS). Discover how ModelOps extends beyond basic model monitoring and tuning capabilities of MLOps, offering a comprehensive 360-degree view of models across the enterprise. Learn about establishing a continuous feedback loop, ensuring reproducibility, compliance, and auditability for business-critical models. Gain insights into best practices for continuous model monitoring, automated remediation to accelerate problem resolution, and creating a feedback loop for ongoing model improvement. Understand how these operational practices can be applied not only to AI models but also to other types of analytical models, providing a unique perspective on model management in the enterprise.

MLOps vs ModelOps – What’s the Difference and Why You Should Care