Managing the Complete Machine learning Lifecycle with MLflow


Webinar date: July 08, 2020

[databricks on-demand artificial inteligence software development webinars ai]


Join us on 8 July for an introductory tutorial on how Databricks can help you manage your end-to-end Machine learning lifecycle.

ML 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 productionise models.

To solve for these challenges, Databricks released MLflow, an open source project that aims at simplifying the entire ML lifecycle. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organisations of any size.

In this tutorial, we will show you how using MLflow can help you:

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

What you will learn:

  • Understand the three main components of open source MLflow (MLflow Tracking, MLflow Projects, MLflow Models) and how each help address challenges of the ML lifecycle.
  • How to use MLflow Tracking to record and query experiments: code, data, config, and results.
  • How to use MLflow Projects packaging format to reproduce runs on any platform.
  • How to use MLflow Models general format to send models to diverse deployment tools.

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