Today we continue our re:Invent series with Srivathsan Canchi, Head of Engineering for the Machine Learning Platform team at Intuit.
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As we teased earlier this week, one of the major announcements coming from AWS at re:Invent was the release of the SageMaker Feature Store. To our pleasant surprise, we came to learn that our friends at Intuit are the original architects of this particular feature store and partnered with AWS to productize it at a much broader scale. In our conversation with Srivathsan, we explore the focus areas that are supported by the Intuit machine learning platform across various teams, including QuickBooks and Mint, Turbotax, and Credit Karma, and his thoughts on why companies should be investing in feature stores.
We also discuss why the concept of “feature store” has seemingly exploded in the last year, and how you know when your organization is ready to deploy one. Finally, we dig into the specifics of the feature store, including the popularity of graphQL and why they chose to include it in their pipelines, the similarities (and differences) between the two versions of the store, and much more!
Thanks to our Sponsor!
Before we get into today’s episode, I want to send a huge thanks to our friends at Amazon Web Services for their support of the podcast and sponsorship of this year’s re:Invent series.
AWS offers a vast array of machine learning services and supporting cloud infrastructure, putting machine learning in the hands of every developer, data scientist, and expert practitioner. It’s extensive set of machine learning services at all three layers of the technology stack offers a broad array capabilities including contact center intelligence, dev/ops tools, industrial machine learning, enterprise search, health analytics, and much more. And, Amazon SageMaker – one of the fastest growing services in AWS history – helps data scientists and developers to prepare, build, train, and deploy high-quality machine learning models quickly by bringing together a broad set of capabilities purpose-built for ML. To learn more about AWS machine learning services, and how they’re helping tens of thousands of customers accelerate their machine learning journeys, visit https://aws.amazon.com/machine-learning/.
Connect with Srivathsan
- Store, Discover, and Share Machine Learning Features with Amazon SageMaker Feature Store
- Scaling MLOps on Kubernetes with Amazon SageMaker Operators
- Paper: Hidden Technical Debt in Machine Learning Systems
- Check out our TWIML Presents: series page!
- Register for the TWIML Newsletter
- Check out the official TWIMLcon:AI Platform video packages here!
- Download our latest eBook, The Definitive Guide to AI Platforms!
“More On That Later” by Lee Rosevere licensed under CC By 4.0