Building Machine Learning Apps

Tuhin Srivastava, (Co-Founder/CEO of @basetenco) talks about enabling Data Scientists to build better Machine Learning models and applications.   

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SHOW NOTES:

• Baseten (homepage)
• Intro to Baseten (video)
• Baseten get $20M in funding from Greylock

Topic 1 - Welcome to the show. Tell us a little bit about your background and why you started Baseten.

Topic 2 - Let’s start by talking about Data Scientists. Not an easy job. They build models and analyze data. But what typically happens after that? How do the models typically get seen and what needed to happen to make that possible?

Topic 3 - What parts of those pain points does Baseten focus on? How does Baseten bring together low-code (or serverless) concepts with the complexity that Data Scientists need to deal with day-to-day?

Topic 4 - What are some of the technologies running behind the scenes to make Baseten easy to use, or that are exposed to the Data Scientists? 

Topic 5 - How does a Data Scientist typically get from “I have a useful model” to “I’m making this part of a useful application”? How does Baseten create building blocks to help them not have to be front-end or back-end engineers?

Topic 6 - What are some of the types of use-cases or applications that your customers have been building?

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