In this talk we will answer the questions on every engineering team’s mind: How do we seamlessly integrate data scientists and their machine learning models into our development workflow? How can data scientists collaborate effectively with each other in a reproducible fashion? How do we empower data scientists to be more like software engineers? Does a cloud native approach help make data science development any easier?
Learn how Red Hat’s AI Center of Excellence (AICoE) integrates CI/CD, gitops and other traditional cloud native concepts into our data science workflows using tools like OpenShift, Kubeflow and Prow in a totally open source cloud environment that empowers teams of data scientists to not only become more integrated into the larger application development life cycle, but to own their models from data collection and exploration through deployment, monitoring and refinement. Ultimately, shortening the gap from a proof of concept locked in some jupyter notebook to a deployed intelligent application.
By the end of this talk, attendees will see some real world implementations of these concepts and learn about the Operate First community cloud environment, which is free and open for everybody to try out all of the topics and techniques discussed in the talk.
Session chairs: Andrei Veselov and Pavel Yadlouski
Michael Clifford is a Data Scientist at Red Hat working in the Office of the CTO on Emerging Technologies, where he works primarily on exploring tools, methodologies and use cases for cloud native data science.
Friday January 28, 2022 5:30pm - 5:55pm CET
Session Room 1
Video Stream