[Webinar] Managing the Complete Machine Learning Lifecycle

Join Databricks Mar 7, 2019, to learn how using MLflow can help you keep track of experiment runs and results across frameworks, execute projects remotely on to a Databricks cluster, and quickly reproduce your runs, and more. Sign up for this webinar now. Sponsored Post.
 
 
Managing the Complete ML Lifecycle
 
[Webinar] Managing the Complete Machine Learning Lifecycle
Thursday, March 7, 2019 @ 10am PST

Machine learning brings new complexities beyond the traditional software development lifecycle. To address these challenges, Databricks unveiled MLflow, an open source project aimed at simplifying the entire machine learning lifecycle. 

MLflow allows companies of all sizes to accelerate the machine learning lifecycle by introducing simple abstractions to package reproducible projects, track results, and encapsulate models. Join this webinar to learn how using MLflow can help you:

  • Keep track of experiment 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.

Featured Speakers
Andy Konwinski, Co-founder and VP of Product at Databricks
Hosted by: Cyrielle Simeone, Product Marketing Manager, Databricks

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Too busy to attend?  Register and we will send you a copy of the on demand presentation.
Sincerely,
The Databricks Team
 
 

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