Your Model Is Not an Island:
Operationalize ML at Scale with MLOps
Operationalizing ML and AI models is hard because data science requires complex technical collaboration with other parts of your data organization. This webinar discusses how MLOps, a derivative of DataOps, orchestrates your entire ML pipeline – from data access to value delivery – for seamless integration of all the heterogeneous data centers, tools, infrastructure, and workflows required for successful model development, deployment, and production. We’ll also cover:
- Why there are so many -Ops terms & how DataOps relates to MLOps & ModelOps;
- How MLOps orchestrates your end-to-end ML pipelines for seamless collaboration between teams; &
- How the use of a DataOps Platform dramatically simplifies model training, deployment, monitoring & governance for operationalization at scale.
About the Speaker
Christopher Bergh is CEO & Head Chef at DataKitchen, a DataOps software & services startup. Chris has more than 30 years of research, software engineering, data analytics & executive management experience. At various points in his career, he has been a COO, CTO, VP & Director of Engineering. He is a recognized expert on DataOps & speaks about DataOps at many industry conferences. Chris is the co-author of The DataOps Manifesto, The DataOps Cookbook & Recipes for DataOps Success. You can follow him on Twitter @ChrisBergh.