What is ML Ops? Solutions and best practices for applying DevOps to production ML services

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, creating a scalable serving infrastructure, and ongoing operation of the ML infrastructure with monitoring and alerting.

Kaz Sato offers an overview of ML Ops (DevOps for ML), sharing solutions and best practices for bringing ML into production service. You’ll learn how to combine Apache Airflow, Kubeflow, and cloud services to build a production ML service infrastructure.