Machine learning development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models.

To solve for these challenges, last June, we unveiled MLflow, an open source platform to manage the complete machine learning lifecycle. Most recently at Spark + AI Summit in San Francisco, we announced the General Availability of Managed MLflow and the upcoming release of MLflow 1.0.  

In this webinar, we have reviewed new and existing MLflow capabilities that allow you to:

• Keep track of experiments runs and results across frameworks.
• Execute projects remotely on to a Databricks cluster, and quickly reproduce your runs.
• Quickly productionize models using Databricks production jobs, Docker containers, Azure ML, or Amazon SageMaker

Notebooks will be provided after this webinar so that you can practice at your own pace.

Presenters