Introducing Kubeflow (with special guests TensorFlow and Apache Spark)
Who is this presentation for?
- Data scientists and data engineers
Data science, machine learning, and artificial intelligence have exploded in popularity in the last five years, but the nagging question of how to put models into production remains. Engineers are typically tasked to build one-off systems to serve predictions that must be maintained amid a quickly evolving backend serving space that evolved from a single machine to custom clusters to “serverless” to Docker to Kubernetes.
Holden Karau presents Kubeflow—an open source project that makes it easy for users to move models from laptop to ML rig to training cluster to deployment. Learn exactly what Kubeflow is, why scalability is so critical for training and model deployment, and more. You’ll leave able to deploy models written in Python’s scikit-learn, R, TensorFlow, Spark, and more. The magic of Kubernetes allows you to write models on your laptop, deploy to an ML rig, and then DevOps can move that model into production with all the bells and whistles, such as monitoring, A/B tests, multiarm bandits, and security.
- Familiarity with Kubernetes and TensorFlow (or other ML libs)
What you'll learn
- Learn what Kubeflow is, modeling training workflows, and deploying models to production
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 Holden is the coauthor of Learning Spark, High Performance Spark, and another Spark book that’s a bit more out of date.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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