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.
- 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
- 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?
- From your perspective, what is the biggest barrier to adoption of machine learning today?
- 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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