Abstract: 

MLOps means different things to different people, however, the fundamental essence of MLOps is to deliver models into productions faster with a consistent, repeatable and reliable approach. Based on our experience of working with various large and small customers across the world, Microsoft has developed an accelerator to do exactly what the word suggests - accelerate our customer's journey to production. Simplicity and segregation of duties are key pillars of this accelerator, which means that our intention is that Data Scientists, ML Engineers and IT teams don't need significant upskilling before they can do MLOps. For example, the Data Scientists just need to focus on the training and inferencing scripts and MLOps will "just work" for them as long as they follow the pattern laid out by the accelerator. While we facilitate for the division of work between ML Engineers and Data Scientists, the accelerator unifies the components in a simple way that is easy for both roles to understand and implement MLOps. Come and hear more about our approach for this accelerator and see a demo (Demo Gods permitting) of this accelerator.

Bio: 

Cindy Weng is a Senior Cloud Solution Architect at Microsoft in Data & AI. She specializes in architecting MLOps solutions for customers across a variety of industries including retail, financial services, consumer goods, and tech. She is one of the authors of the MLOps V2 unified accelerator by Microsoft.