Ship it: A practitioner's guide to model management and deployment with Kubeflow
Who is this presentation for?Data engineers, data architects, developers
Model serving and model metadata management are the two skills that separate the AI engineers and the data scientists. However, there’s a shockingly limited number of tools and guides on how to do this effectively (especially compared to the effectively infinite galaxy of tutorials on long short-term memories (LSTMs), recurrent neural networks (RNNs), convolutional neural network (CNNs), and other data science and machine learning (ML) models).
Kubeflow has a suite of tools that address these two areas of AI engineering where so few choose to tread. Trevor Grant and Holden Karau discuss how to use Kubeflow’s Artifact Store (model metadata management tool) and KFServing (Kubeflow’s model serving abstraction). You’ll explore the mechanics of each piece and discover practical tips and tricks for using the tools in Kubeflow.
- A basic understanding of ML
What you'll learn
- Discover the basics of Kubeflow's model management and deployment options
Holden Karau is a transgender Canadian software engineer working in the bay area. Previously, she worked at IBM, Alpine, Databricks, Google (twice), Foursquare, and Amazon. Holden is the coauthor of Learning Spark, High Performance Spark, and another Spark book that’s a bit more out of date. She’s a committer on the Apache Spark, SystemML, and Mahout projects. When not in San Francisco, Holden speaks internationally about different big data technologies (mostly Spark). She was tricked into the world of big data while trying to improve search and recommendation systems and has long since forgotten her original goal. Outside of work, she enjoys playing with fire, riding scooters, and dancing.
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