#MachineLearning #MLProduction #FeatureEngineering
Chip Huyen, co-founder of Claypot AI and author of O'Reilly's best-selling "Designing Machine Learning Systems" joins @JonKrohnLearns to share her expertise on designing production-ready machine learning applications, the importance of iteration in real-world deployment, and the critical role of real-time machine learning in various applications. Technical listeners like data scientists and machine learning engineers will definitely enjoy this one!
This episode is brought to you by Pathway, the reactive data processing framework ([ Ссылка ]), and by epic LinkedIn Learning instructor Keith McCormick ([ Ссылка ]). Interested in sponsoring a SuperDataScience Podcast episode? Visit [ Ссылка ] for sponsorship information.
In this episode you will learn:
• [00:00:00] Why Chip wrote 'Designing Machine Learning Systems'
• [00:11:37] How Chip ended up teaching at Stanford
• [00:19:32] About Chip's book 'Designing Machine Learning Systems'
• [00:29:13] What makes ML feel like magic
• [00:36:16] How to align business intent, context, and metrics with ML
• [00:40:23] The lessons Chip learned about training data
• [00:51:40] Chip's secrets to engineering good features
• [01:06:09] How Chip optimizes her productivity
Additional materials: [ Ссылка ]
661: Designing Machine Learning Systems — with Chip Huyen
Теги
SuperDataSciencePodcastSuper Data Science PodcastData ScienceJon Krohndesigning machine learning systemsdesigning ml systemschip huyenchip huyen designing machine learning systemschip huyen machine learningchip huyen stanfordchip huyen bookreal time machine learningtraining data in machine learningfeature engineering in machine learningproduction ready machine learningmachine learning model in productionclaypot ai
