December 14 | 10:00 AM PT | 12:00 PM CT | 1:00 PM ET
As organizations continue to develop their machine learning (ML) practice, there’s a growing need for robust and reliable platforms capable of handling the entire ML lifecycle. The emergence of MLOps is promising, but many challenges remain.
Join our interactive event to hear about the latest developments from Databricks geared toward automating MLOps — including new Git and CI/CD integrations, autologging of experiments, model explainability and model serving.
We’ll also cover:
Best practices from domain experts to operationalize ML at scale, from experimentation to production
A checklist of the capabilities you’ll need, common pitfalls, as well as technological and organizational challenges — and how to overcome them
Presentations will be enhanced with demos, success stories and learnings from experts who have deployed these pipelines for predictive analytics. A live Q&A and discussion will keep this event engaging for data science practitioners and leaders alike.
Agenda (PT) 10:00–10:05 AM Intro to Databricks for Machine Learning 10:05–10:20 AM The Full ML Lifecycle From Experimentation to Production — and How to Operationalize ML at Scale Using MLOps 10:20–10:50 AM Demo on the Databricks Environment 10:50–11:00 AM Q&A
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