Stop Feeding Garbage Data To Your ML Models, Clean It Up With Galileo

Summary
Machine learning is a force multiplier that can generate an outsized impact on your organization. Unfortunately, if you are feeding your ML model garbage data, then you will get orders of magnitude more garbage out of it. The team behind Galileo experienced that pain for themselves and have set out to make data management and cleaning for machine learning a first class concern in your workflow. In this episode Vikram Chatterji shares the story of how Galileo got started and how you can use their platform to fix your ML data so that you can get back to the fun parts.
Announcements

• Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.
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• Your host is Tobias Macey and today I’m interviewing Vikram Chatterji about Galileo, a platform for uncovering and addressing data problems to improve your model quality

Interview

• Introduction
• How did you get involved in machine learning?
• Can you describe what Galileo is and the story behind it?
• Who are the target users of the platform and what are the tools/workflows that you are replacing? 
  • How does that focus inform and influence the design and prioritization of features in the platform?
• What are some of the real-world impacts that you have experienced as a result of the kinds of data problems that you are addressing with Galileo?
• Can you describe how the Galileo product is implemented? 
  • What are some of the assumptions that you had formed from your own experiences that have been challenged as you worked with early design partners?
• The toolchains and model architectures of any given team is unlikely to be a perfect match across departments or organizations. What are the core principles/concepts that you have hooked into in order to provide the broadest compatibility? 
  • What are the model types/frameworks/etc. that you have had to forego support for in the early versions of your product?
• Can you describe the workflow for someone building a machine learning model and how Galileo fits across the various stages of that cycle? 
  • What are some of the biggest difficulties posed by the non-linear nature of the experimentation cycle in model development?
• What are some of the ways that you work to quantify the impact of your tool on the productivity and profit contributions of an ML team/organization?
• What are the most interesting, innovative, or unexpected ways that you have seen Galileo used?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on Galileo?
• When is Galileo the wrong choice?
• What do you have planned for the future of Galileo?

Contact Info

• LinkedIn

Parting Question

• From your perspective, what is the biggest barrier to adoption of machine learning today?

Closing Announcements

• Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
• Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
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Links

• Galileo
• F1 Score
• Tensorflow
• Keras
• SpaCy
  • Podcast.__init__ Episode
• Pytorch
  • Podcast.__init__ Episode
• MXNet
• Jax

The intro and outro music is from Hitman’s Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0