Using Airflow with Tensorflow and MLFlow

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  • Kenten Danas Kenten Danas Lead Developer Advocate
  • Jeff Fletcher Jeff Fletcher Director of Field Engineering EMEA

By: Kenten Danas, Lead Developer Advocate, Astronomer Jeff Fletcher, Director of Field Engineering, EMEA, Astronomer

In this webinar, we provide viewers with a hands-on, in-depth look at how to manage machine learning pipelines in production with Airflow. Specifically, we consider a special use case that integrates Tensorflow and MLFlow, among other tools, with Airflow, to prepare, train, track, and serve an image classifier model.

This webinar shows you everything you need to know about incorporating Tensorflow and MLFlow into your machine learning pipelines, and answers questions like:

  • What are “good” machine learning operations?

  • How do you automate your machine learning operations with Airflow?

  • What is needed to prepare images for model training?

  • How do you train your model using Tensorflow?

  • How do you track your model using MLFlow?

  • How do you present the model as a real-time API endpoint?

All of the sample code shown in this webinar can be found in this repo.

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