Alex Casalboni | Building Serverless Machine Learning Models in the Cloud
1,872 views · Published 24 October 2016 · 40:41 · Indexed 4 November 2026
Channel: PyData · 2016 · Science & Technology
PyData DC 2016 You’ll learn how to efficiently design and train machine learning models in Python and deploy them to the cloud. This process reduces the development & operational efforts required to make your prototypes production-ready. We will describe the main challenges faced by data scientists involved in deploying machine learning models into real production environments with specific references, examples of Python libraries, and multi-model systems requiring advanced features such as A/B testing and high scalability & availability. While discussing the advantages and limitations of multiple deployment strategies in the cloud, we will focus on serverless computing (i.e. AWS Lambda) as a solution for simplifying your development & deployment workflows. 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps
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