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.

Graft LogoGraft™ is a cloud-native platform that aims to make the AI of the 1% accessible to the 99%. Wield the most advanced techniques for unlocking the value of data, including text, images, video, audio, and graphs. No machine learning skills required, no team to hire, and no infrastructure to build or maintain.

For more information on Graft or to schedule a demo go to themachinelearningpodcast.com/graft today! And tell them Tobias sent you.


Deepchecks LogoBuilding good ML models is hard, but testing them properly is even harder. At Deepchecks, they built an open-source testing framework that follows best practices, ensuring that your models behave as expected. Get started quickly using their built-in library of checks for testing and validating your model’s behavior and performance, and extend it to meet your specific needs as your model evolves. Accelerate your machine learning projects by building trust in your models and automating the testing that you used to do manually. Go to themachinelearningpodcast.com/deepchecks today to get started!


Predibase’s founders saw the pain of getting ML models developed and in-production, taking up to a year even at leading tech companies like Uber, so they built internal platforms that drastically lowered the time-to-value and increased access. The key was taking a “declarative approach” to machine learning, which Piero Molino (CEO) introduced with Ludwig, an open source framework to create deep learning models with 8,400+ GitHub stars, more than 100 contributors, and thousands of monthly downloads. With Ludwig, tasks that took months-to-years were handed off to teams in thirty minutes and just six lines of human-readable configuration that can define an entire machine learning pipeline.

Now with Predibase, we are bringing the power of declarative machine learning built on top of Ludwig to broader organizations with our enterprise platform. Like Infrastructure as Code simplified IT, Predibase’s machine learning (ML) platform allows users to focus on the “what” of their ML models rather than the “how”, breaking free of the usual limits in low-code systems and bringing down the time-to-value of ML projects from years to days. Go to themachinelearningpodcast.com/predibase today to learn more and try it for yourself!


Announcements

  • Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.
  • Predibase is a low-code ML platform without low-code limits. Built on top of our open source foundations of Ludwig and Horovod, our platform allows you to train state-of-the-art ML and deep learning models on your datasets at scale. Our platform works on text, images, tabular, audio and multi-modal data using our novel compositional model architecture. We allow users to operationalize models on top of the modern data stack, through REST and PQL – an extension of SQL that puts predictive power in the hands of data practitioners. Go to themachinelearningpodcast.com/predibase today to learn more and try it out!
  • Do you wish you could use artificial intelligence to drive your business the way Big Tech does, but don’t have a money printer? Graft™ is a cloud-native platform that aims to make the AI of the 1% accessible to the 99%. Wield the most advanced techniques for unlocking the value of data, including text, images, video, audio, and graphs. No machine learning skills required, no team to hire, and no infrastructure to build or maintain. For more information on Graft or to schedule a demo, visit themachinelearningpodcast.com/graft today and tell them Tobias sent you.
  • Building good ML models is hard, but testing them properly is even harder. At Deepchecks, they built an open-source testing framework that follows best practices, ensuring that your models behave as expected. Get started quickly using their built-in library of checks for testing and validating your model’s behavior and performance, and extend it to meet your specific needs as your model evolves. Accelerate your machine learning projects by building trust in your models and automating the testing that you used to do manually. Go to themachinelearningpodcast.com/deepchecks today to get started!
  • 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

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.
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  • If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@themachinelearningpodcast.com) with your story.
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Links

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

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