Companies are realizing the value of using ML models to drive better outcomes for their businesses. Harnessing the predictive power of their data with ML models to remain competitive is becoming more critical to business operations, yet 60% of ML models never make it to production. This is due to complications often exacerbated by outdated business processes and the fact that it's not a core competency for typical data science teams.

In this webinar, we will explore these specific challenges and illustrate how familiar cloud services can be stitched together with MLOps Foundations, enabling rapid development, training, scoring and deployment of models across multiple environments with strong governance and security controls to provide a foundation for successful model lifecycle management.

What we'll cover:
- Introduction to MLOps Foundations powered by Model Factory
- The gap between the Data Scientists and ML Operations
- The distinction between MLOps and DevOps
- Architecture patterns necessary for elements of effective MLOps
- How a “model factory” architecture holistically addresses CI/CD for ML

Demo that will explore:
- Quick feedback and traceability for model development
- ML framework agnostic tooling for packaging of models
- Platform agnostic continuous/rolling deployment