ML ops and Kubeflow pipelines

This will be presented in English.

Kaz Sato (Google)
14:0014:40 Friday, June 21, 2019
Average rating: *****
(5.00, 1 rating)

必要预备知识 (Prerequisite Knowledge)

  • A basic understanding of machine learning (ML)

您将学到什么 (What you'll learn)

  • Learn how DevOps for ML (ML ops) is the largest challenge on bringing ML to production
  • See how Google provides the best practice and platform

描述 (Description)

Creating an ML model is just a starting point. To bring the technology into production service, you need to solve various real-world issues such as building a data pipeline for continuous training, automated validation of the model, version control of the model, scalable serving infra, and ongoing operation of the ML infra with monitoring and alerting. Kaz Sato discusses ML ops concepts and explains how to combine Kubeflow pipelines to build a production ML service infra.

Photo of Kaz Sato

Kaz Sato


Kaz Sato is a staff developer advocate on the cloud platform team at Google, where he leads the developer advocacy team for machine learning and data analytics products such as TensorFlow, the Vision API, and BigQuery. Kaz has been leading and supporting developer communities for Google Cloud for over seven years. He’s a frequent speaker at conferences, including Google I/O 2016, Hadoop Summit 2016 San Jose, Strata + Hadoop World 2016, and Google Next 2015 NYC and Tel Aviv, and he has hosted FPGA meetups since 2013.