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End to End ML with Kubeflow: Scaling with Big & Tiny Data (+ deep learning of course) - SIGNAL 2018

1,810 views · Published 29 October 2018 · 28:35 · Indexed 27 September 2026

Channel: Twilio · 2018 · Science & Technology

There are many great tools for training machine learning tools, ranging from sci-kit to Apache Spark, and tensorflow. However many of these systems largely leave open the question how to use our models outside of the batch world (like in a reactive application). Different options exist for persisting the results and using them for live training, and we will explore the trade-offs of the different formats and their corresponding serving/prediction layers. From there we choose two formats and illustrate how to build an auto-scaling reactive serving layer. We'll tie this all together with Kubeflow as well as showing how it can be used with non-Spark systems. Just building an end-to-end pipeline like this isn’t enough to be able to take your model to production, so we will wrap up with important pointers for things like updating models, validation, and other little details that often get overlooked since they don’t fit nicely on a slide with a cat picture (not that we won’t try). Time permitting we’ll talk about designing clients for graceful degradation when everything eventually catches on fire.

End to End ML with Kubeflow: Scaling with Big & Tiny Data (+ deep learning of course) - SIGNAL 2018

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