# Real-Time Machine Learning Has Entered The Realm Of The Possible

Page: https://stenobird.com/podcast/ai-engineering-podcast/real-time-machine-learning-has-entered-the-realm-of-the-possible
Text version: https://stenobird.com/podcast/ai-engineering-podcast/real-time-machine-learning-has-entered-the-realm-of-the-possible.md
Podcast: [AI Engineering Podcast](https://stenobird.com/podcast/ai-engineering-podcast)
Published: 2023-03-09T22:00:00+00:00
Episode link: https://www.aiengineeringpodcast.com/tecton-real-time-machine-learning-episode-17
Audio file: https://op3.dev/e/dts.podtrac.com/redirect.mp3/serve.podhome.fm/episode/f6ff0caa-931b-4c08-bfdd-08dc7f5cd336/638530538549358146df1881a1-5ea8-4c1c-9696-4bb4499b91ecv2.mp3
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JSON: https://stenobird.com/v1/public/podcasts/ai-engineering-podcast/episodes/real-time-machine-learning-has-entered-the-realm-of-the-possible
Duration seconds: 2070

## Resource

Summary Machine learning models have predominantly been built and updated in a batch modality. While this is operationally simpler, it doesn't always provide the best experience or capabilities for end users of the model. Tecton has been investing in the infrastructure and workflows that enable building and updating ML models with real-time data to allow you to react to real-world events as they happen. In this episode CTO Kevin Stumpf explores they benefits of real-time machine learning and the systems that are necessary to support the development and maintenance of those models. 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 Kevin Stumpf about the challenges and promise of real-time ML applications Interview Introduction How did you get involved in machine learning? Can you describe what real-time ML is and some examples of where it might be applied? What are the operational and organizational requirements for being able to adopt real-time approaches for ML projects? What are some of the ways that real-time requirements influence the scale/scope/architecture of an ML model? What are some of the failure modes for real-time vs analytical or operational ML? Given the low latency between source/input data being generated or received and a prediction being generated, how does that influence susceptibility to e.g. data drift?  Data quality and accuracy also become more critical. What are some of the validation strategies that teams need to consider as they move to real-time? What are the most interesting, innovative, or unexpected ways that you have seen real-time ML applied? What are the most interesting, unexpected, or…

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