Wednesday 3:15 PM–4:00 PM in Track 2 Room

To Production and Beyond: How to Manage the Machine Learning Lifecycle with MLflow

Amanda Moran

Audience level:
Intermediate

Description

Building a machine learning model that runs locally on a laptop probably isn't generating any value, you have to get that model into production. This talk will focus on getting Data Scientist and Data Engineers more comfortable with the Machine Learning Lifecycle, and how the open source tool MLflow can help. Let's take our machine learning models to production and beyond!

Abstract

Introduction

What is MLflow?

What is model tracking?

How to build a reproducible project?

How to create models that can be run anywhere?

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