{"podcast":{"title":"AI Engineering Podcast","slug":"ai-engineering-podcast","podcast_index_feed_id":5875646,"rss_url":"https://serve.podhome.fm/rss/c9abdd38-a5dc-5eb2-96fd-f833f93208a7","website_url":"https://www.aiengineeringpodcast.com","image_url":"https://assets.podhome.fm/f6ff0caa-931b-4c08-bfdd-08dc7f5cd336/638557211890591941ai_engineering_podcast_logo.jpg","author":"Tobias Macey","episode_count":79,"summary":"This show is your guidebook to building scalable and maintainable AI systems. You will learn how to architect AI applications, apply AI to your work, and the considerations involved in building or customizing new models. Everything that you need to know to deliver real impact and value with machine learning and artificial intelligence.","last_synced_at":null,"page_url":"https://stenobird.com/podcast/ai-engineering-podcast"},"episode":{"title":"Using Generative AI To Accelerate Feature Engineering At FeatureByte","slug":"using-generative-ai-to-accelerate-feature-engineering-at-featurebyte","published_at":"2024-02-11T22:00:00+00:00","page_url":"https://stenobird.com/podcast/ai-engineering-podcast/using-generative-ai-to-accelerate-feature-engineering-at-featurebyte","show_page_url":"https://stenobird.com/podcast/ai-engineering-podcast","url":"https://www.aiengineeringpodcast.com/episodepage/29","audio_url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/serve.podhome.fm/episode/f6ff0caa-931b-4c08-bfdd-08dc7f5cd336/63853053836951690244e27f34-de10-4f11-a76b-35cc95bef497.mp3","summary":"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 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 Interview 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…","meta_description":"Summary One of the most time consuming aspects of building a machine learning model is feature engineering. Generative AI offers the possibility of accele…","key_points":[],"chapters":[],"topics":[],"duration_seconds":2699,"processing_state":"failed","actions":[{"name":"request_transcript","method":"POST","url":"https://stenobird.com/v1/public/podcasts/ai-engineering-podcast/episodes/using-generative-ai-to-accelerate-feature-engineering-at-featurebyte/transcription-requests","description":"Idempotently request low-priority transcript generation for this episode."},{"name":"read_markdown","method":"GET","url":"https://stenobird.com/podcast/ai-engineering-podcast/using-generative-ai-to-accelerate-feature-engineering-at-featurebyte.md","description":"Read the agent-friendly Markdown representation of this episode resource."}]}}