What Is ML Ops: Solutions and Best Practices for DevOps of Production ML Services

Wednesday, June 12, 2019 - 2:00 pm3:00 pm

Kaz Sato, Google

Abstract: 

This session features the concept of "ML Ops" (DevOps for ML), solutions and best practices bringing ML into production service. We will learn how to combine Google Cloud Kubeflow Pipelines for building a data pipeline for continuous training and validation, version control, scalable serving, and ongoing monitoring and alerting.

Kaz Sato, Google

Kaz Sato is a Staff Developer Advocate at Google Cloud for machine learning and data analytics products such as TensorFlow, Cloud ML, and BigQuery. Kaz has been invited as a speaker at major events including Google Cloud Next, Google I/O, Strata, NVIDIA GTC, etc. He has authored many GCP blog posts supporting developer communities for Google Cloud for over eight years. He is also interested in hardware and IoT and has been hosting FPGA meetups since 2013.

BibTeX
@conference {233297,
author = {Kaz Sato},
title = {What Is {ML} Ops: Solutions and Best Practices for DevOps of Production {ML} Services},
year = {2019},
address = {Singapore},
publisher = {{USENIX} Association},
}