TWIMLcon 2019


Productionizing Machine Learning Models at Scale with Kubernetes

Case Study

Productionizing machine learning models in an organization is a difficult challenge for several reasons. From a technical perspective, it requires tooling to handle tasks such as model deployment, monitoring, and retraining. Talent-wise, these tasks require practitioners to possess technical skills in software engineering and DevOps. Coupled with a rapidly changing landscape and shortage of established best practices, operationalizing models is no small feat. Kubernetes provides machine learning practitioners the ability to deploy their model training and inference processes, scale deployed models vertically and horizontally, and can be extended to cover use-cases including model monitoring and A/B testing. The goal of this presentation is to discuss how Kubernetes can be leveraged to train, deploy, and monitor models in production settings. Throughout the talk we’ll reveal the technical and organizational lessons learned from using Kubernetes to productionize machine learning workloads at 2U.

Session Speakers

Director Data Science

Oops, please Login or Create Account to view On Demand

The good news is that it's both easy and free to register and get access.

Account Login

Create Account

Newsletter Consent(Required)
Terms and Privacy Consent
This field is for validation purposes and should be left unchanged.