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WWT Research • Applied Research Report
• 14 minute read

Deploying MLOps Platform to Enable End-to-End ML Workflows

Deploying the Kubeflow MLOps platform in AWS to enabled our Data Science team to create end-to-end ML workflows for automated delivery of machine-learning models.

This was originally published in April 2021

 Abstract

In this white paper, you will learn about the MLOps platform that a WWT machine-learning (ML) platform infrastructure team built to reliably deliver trained and validated ML models into production. By deploying the Kubeflow MLOps platform in AWS as a component of our common ML infrastructure, the team enabled WWT data scientists to create end-to-end ML workflows. As part of the MLOps platform deployment, the team built an automated delivery pipeline proof-of-concept to train and productionize a natural language processing (NLP) deep learning model, along with microservices that enable a user to search for relevant WWT platform articles that have been ranked by that productionized model.

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