Using MLflow to Manage the Machine Learning Life Cycle

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As data science teams adopt tools for experimentation, deployment, and scale out their teams, MLflow can serve as a powerful tool to integrate the development of AI models and the overall platform surrounding them. MLflow is an open-source platform to manage the machine learning lifecycle, and in this talk, we will show how this tool can be leveraged in Databricks to track experiments from multiple runs, reproduce results, perform remote runs and deploy models for real-time testing.

About the Speaker:
Ricardo Portilla works at Databricks as a Solutions Architect. He completed his PhD in Mathematics at the University of Michigan, and after that led Spark migrations, engineered various solutions in Spark on large-scale financial data, and more recently focused on data science at scale using time series analysis and unsupervised learning methods. He is passionate about enabling data science on the Databricks platform and showing MLflow in action for model lifecycle management.

6:30pm – 7:00pm Networking and Refreshments
7:00pm – 7:10pm Introduction, Announcements
7:10pm – 7:40pm Presentation
7:40pm – 7:55pm Q&A
8:00pm – 8:30pm Data Drinks @Tonic (2036 G St NW)