Deploying a Machine Learning Project with MLflow Projects

Summary

This course shows you how to train and deploy a large scale machine learning model using MLflow and Apache Spark.

Description

In this course, we’ll show you how to train and deploy a large scale machine learning model using MLflow and Apache Spark.  

 

This course is the third in a series of three courses developed to show you how to use Databricks to work with a single data set from experimentation to production-scale machine leaning model deployment. We recommend taking the first two courses in this series before continuing with this course: 

 

  • Building and Deploying Machine Learning Models: The Bias-Variance Tradeoff

  • Tracking Experiments with MLflow

 

Learning objectives

  • Summarize Databricks best practices for deploying machine learning projects with MLflow. 

  • Explain local development strategies for writing software with Databricks.

  • Use Databricks to write production-grade machine learning software.

Prerequisites

  • Beginning-level experience running data science workflows in the Databricks Workspace

  • Beginner-level experience with Apache Spark

  • Intermediate-level experience with the Scipy Numerical Stack

  • Intermediate-level experience with the command line