Using Generative AI To Accelerate Feature Engineering At FeatureByte
Update: 2024-02-11
Description
Summary
One of the most time consuming aspects of building a machine learning model is feature engineering. Generative AI offers the possibility of accelerating the discovery and creation of feature pipelines. In this episode Colin Priest explains how FeatureByte is applying generative AI models to the challenge of building and maintaining machine learning pipelines.
Announcements
Parting Question
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One of the most time consuming aspects of building a machine learning model is feature engineering. Generative AI offers the possibility of accelerating the discovery and creation of feature pipelines. In this episode Colin Priest explains how FeatureByte is applying generative AI models to the challenge of building and maintaining machine learning pipelines.
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 Colin Priest about applying generative AI to the task of building and deploying AI pipelines
- Introduction
- How did you get involved in machine learning?
- Can you start by giving the 30,000 foot view of the steps involved in an AI pipeline?
- Understand the problem
- Feature ideation
- Feature engineering
- Experiment
- Optimize
- Productionize
- What are the stages of that process that are prone to repetition?
- What are the ways that teams typically try to automate those steps?
- What are the features of generative AI models that can be brought to bear on the design stage of an AI pipeline?
- What are the validation/verification processes that engineers need to apply to the generated suggestions?
- What are the opportunities/limitations for unit/integration style tests?
- What are the elements of developer experience that need to be addressed to make the gen AI capabilities an enhancement instead of a distraction?
- What are the interfaces through which the AI functionality can/should be exposed?
- What are the aspects of pipeline and model deployment that can benefit from generative AI functionality?
- What are the potential risk factors that need to be considered when evaluating the application of this functionality?
- What are the most interesting, innovative, or unexpected ways that you have seen generative AI used in the development and maintenance of AI pipelines?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on the application of generative AI to the ML workflow?
- When is generative AI the wrong choice?
- What do you have planned for the future of FeatureByte's AI copilot capabiliteis?
Parting Question
- 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.
- 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.
- To help other people find the show please leave a review on iTunes and tell your friends and co-workers.
- FeatureByte
- Generative AI
- The Art of War
- OCR == Optical Character Recognition
- Genetic Algorithm
- Semantic Layer
- Prompt Engineering
Support The Machine Learning Podcast
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