MLOps Virtual Event
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.
Tristan Nixon is a Solutions Architect with Databricks. He was born and raised in Toronto, Canada, where he studied Cognitive Science & A.I. under such luminaries of the field as Geoffrey Hinton, one of the fathers of neural networks. He has worked in data engineering and data science for the past 15 years, across a broad range of industries from education and telecommunications to chemicals manufacturing among others. He specializes in MLOps, natural language processing and timeseries forecasting. He now lives in sunny southern California, and is learning to sail.
Digan is a Solutions Architect at Databricks helping customers understand the Databricks platform. He cares about delivering high-value outcomes for the most important business challenges while providing an unparalleled customer experience. He’s passionate about learning new technologies and recognizing how they can be leveraged to solve organizations’ business problems.