The Python Podcast.init

Build A Full Stack ML Powered App In An Afternoon With Baseten

Nov 21 '22

Preamble

This is a cross-over episode from our new show The Machine Learning Podcast, the show about going from idea to production with machine learning.

Summary

Building an ML model is getting easier than ever, but it is still a challenge to get that model in front of the people that you built it for. Baseten is a platform that helps you quickly generate a full stack application powered by your model. You can easily create a web interface and APIs powered by the model you created, or a pre-trained model from their library. In this episode Tuhin Srivastava, co-founder of Basten, explains how the platform empowers data scientists and ML engineers to get their work in production without having to negotiate for help from their application development colleagues.

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 Tuhin Srivastava about Baseten, an ML Application Builder for data science and machine learning teams

Interview

Introduction
How did you get involved in machine learning?
Can you describe what Baseten is and the story behind it?
Who are the target users for Baseten and what problems are you solving for them?
What are some of the typical technical requirements for an application that is powered by a machine learning model?
- In the absence of Baseten, what are some of the common utilities/patterns that teams might rely on?
What kinds of challenges do teams run into when serving a model in the context of an application?
There are a number of projects that aim to reduce the overhead of turning a model into a usable product (e.g. Streamlit, Hex, etc.). What is your assessment of the current ecosystem for lowering the barrier to product development for ML and data science teams?
Can you describe how the Baseten platform is designed?
- How have the design and goals of the project changed or evolved since you started working on it?
- How do you handle sandboxing of arbitrary user-managed code to ensure security and stability of the platform?
How did you approach the system design to allow for mapping application development paradigms into a structure that was accessible to ML professionals?
Can you describe the workflow for building an ML powered application?
What types of models do you support? (e.g. NLP, computer vision, timeseries, deep neural nets vs. linear regression, etc.)
- How do the monitoring requirements shift for these different model types?
- What other challenges are presented by these different model types?
What are the limitations in size/complexity/operational requirements that you have to impose to ensure a stable platform?
What is the process for deploying model updates?
For organizations that are relying on Baseten as a prototyping platform, what are the options for taking a successful application and handing it off to a product team for further customization?
What are the most interesting, innovative, or unexpected ways that you have seen Baseten used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Baseten?
When is Baseten the wrong choice?
What do you have planned for the future of Baseten?

Contact Info

@tuhinone on Twitter
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.
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