Your Model is Not An Island:
Operationalize Machine Learning at Scale with ModelOps
Operationalizing ML and AI models is hard because data science requires complex technical collaboration with other parts of your data organization. Join our webinar to discuss how ModelOps, 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 and how DataOps relates to ModelOps and MLOps;
- How ModelOps orchestrates your end-to-end ML pipelines for seamless collaboration between teams; and
- How use of a DataOps Platform dramatically simplifies model training, deployment, monitoring, and governance for operationalization at scale.
About the Speaker
Chris Bergh is the CEO and Head Chef at DataKitchen. Chris has more than 25 years of research, software engineering, data analytics, and executive management experience. At various points in his career, he has been a COO, CTO, VP, and Director of Engineering. Chris is a recognized expert on DataOps. He is the co-author of the "DataOps Cookbook” and the “DataOps Manifesto,” and a speaker on DataOps at many industry conferences.