Beginning MLOps with MLFlow: Deploy Models in AWS SageMaker, Google Cloud, and Microsoft Azure

© Sridhar Alla, Suman Kalyan Adari 2021

S. Alla, S. K. AdariBeginning MLOps with MLFlowhttps://doi.org/10.1007/978-1-4842-6549-9_4

4. Introduction to MLFlow

Sridhar Alla¹   and Suman Kalyan Adari²

(1)

Delran, NJ, USA

(2)

Tampa, FL, USA

 

In this chapter, we will cover what MLFlow is, what it does, and how you can implement MLOps setups into your existing projects. More specifically, we will cover how you can integrate MLFlow with scikit-learn, TensorFlow 2.0+/Keras, PyTorch, and PySpark. We will go over experiment creation; metric, parameter, and artifact logging; model logging; and how you can deploy models on a local server and query them for predictions.

Introduction

In the previous chapter, we went over what an optimal MLOps setup looks like. However, ...