This chapter discusses the various capabilities of the platform in terms of machine learning and highlights its flexibility to support different types of machine learning problems. The speakers also mention the current common use case of Natural Language Processing (NLP) and the scalability advantage it offers to teams.
Summary
The focus of machine learning projects has long been the model that is built in the process. As AI powered applications grow in popularity and power, the model is just the beginning. In this episode Josh Tobin shares his experience from his time as a machine learning researcher up to his current work as a founder at Gantry, and the shift in focus from model development to machine learning systems.
Announcements
- Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.
- Your host is Tobias Macey and today I'm interviewing Josh Tobin about the state of industry best practices for designing and building ML models
Interview
- Introduction
- How did you get involved in machine learning?
- Can you start by describing what a "traditional" process for building a model looks like?
- What are the forces that shaped those "best practices"?
- What are some of the practices that are still necessary/useful and what is becoming outdated?
- What are the changes in the ecosystem (tooling, research, communal knowledge, etc.) that are forcing teams to reconsider how they think about modeling?
- What are the most critical practices/capabilities for teams who are building services powered by ML/AI?
- What systems do they need to support them in those efforts?
- Can you describe what you are building at Gantry and how it aids in the process of developing/deploying/maintaining models with "modern" workflows?
- What are the most challenging aspects of building a platform that supports ML teams in their workflows?
- What are the most interesting, innovative, or unexpected ways that you have seen teams approach model development/validation?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Gantry?
- When is Gantry the wrong choice?
- What are some of the resources that you find most helpful to stay apprised of how modeling and ML practices are evolving?
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
