Using AI To Transform Your Business Without The Headache Using Graft

Summary

Machine learning is a transformative tool for the organizations that can take advantage of it. While the frameworks and platforms for building machine learning applications are becoming more powerful and broadly available, there is still a significant investment of time, money, and talent required to take full advantage of it. In order to reduce that barrier further Adam Oliner and Brian Calvert, along with their other co-founders, started Graft. In this episode Adam and Brian explain how they have built a platform designed to empower everyone in the business to take part in designing and building ML projects, while managing the end-to-end workflow required to go from data to production.

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!
  • 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 Brian Calvert and Adam Oliner about Graft, a cloud-native platform designed to simplify the work of applying AI to business problems

Interview

  • Introduction
  • How did you get involved in machine learning?
  • Can you describe what Graft is and the story behind it?
  • What is the core thesis of the problem you are targeting?
    • How does the Graft product address that problem?
    • Who are the personas that you are focused on working with both now in your early stages and in the future as you evolve the product?
  • What are the capabilities that can be unlocked in different organizations by reducing the friction and up-front investment required to adopt ML/AI?
    • What are the user-facing interfaces that you are focused on providing to make that adoption curve as shallow as possible?
      • What are some of the unavoidable bits of complexity that need to be surfaced to the end user?
  • Can you describe the infrastructure and platform design that you are relying on for the Graft product?
    • What are some of the emerging "best practices" around ML/AI that you have been able to build on top of?
      • As new techniques and practices are discovered/introduced how are you thinking about the adoption process and how/when to integrate them into the Graft product?
    • What are some of the new engineering challenges that you have had to tackle as a result of your specific product?
  • Machine learning can be a very data and compute intensive endeavor. How are you thinking about scalability in a multi-tenant system?
    • Different model and data types can be widely divergent in terms of the cost (monetary, time, compute, etc.) required. How are you thinking about amortizing vs. passing through those costs to the end user?
  • Can you describe the adoption/integration process for someone using Graft?
    • Once they are onboarded and they have connected to their various data sources, what is the workflow for someone to apply ML capabilities to their problems?
  • One of the challenges about the current state of ML capabilities and adoption is understanding what is possible and what is impractical. How have you designed Graft to help identify and expose opportunities for applying ML within the organization?
  • What are some of the challenges of customer education and overall messaging that you are working through?
  • What are the most interesting, innovative, or unexpected ways that you have seen Graft used?
  • What are the most interesting, unexpected, or challenging lessons that you have learned while working on Graft?
  • When is Graft the wrong choice?
  • What do you have planned for the future of Graft?

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.
  • Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
  • 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](https://creativecommons.org/licenses/by-sa/3.0/

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