MLOps & Automation Workshop: Bringing ML to Production in a Few Easy Steps

The process of moving from data-science research to a full-blown production pipelines is long and resource consuming. New practices like MLOps, and tools like Kubeflow (ML toolkit and pipeline management over Kubernetes) are emerging to provide the equivalent of CI/CD for data science projects, but this requires dedicated ML engineering teams to translate the data-scientists / engineers work to production ready code.

Serverless can simplify data science by automating the process of code to container and enables users to add instrumentation and auto-scaling with minimum overhead. However, serverless has many limitations involving performance, lack of concurrency, lack of GPU support, limited application patterns and limited debugging possibilities. In this session Yaron Haviv discusses Kubeflow, and how it works with Nuclio – an open source serverless functions framework and MLRun – an open source MLOps orchestration framework, enabling serverless data science and full ML lifecycle automation over Kubeflow.

Yaron shows real-world examples and a live demo and explains how these open source tools can significantly accelerate time to market and save resources on your ML projects.