Implementing a Machine Learning Solution with Microsoft Azure Databricks Training

Objectives

Provision an Azure Databricks workspace and cluster

Use Azure Databricks to train a machine learning model

Use MLflow to track experiments and manage machine learning models

Integrate Azure Databricks with Azure Machine Learning

Audience

This course is designed for data scientists with experience of Pythion who need to learn how to apply their data science and machine learning skills on Azure Databricks

Prerequisites

Before attending this course, you should have experience of using Python to work with data, and some knowledge of machine learning concepts. Before attending this course, complete the following learning path on Microsoft Learn:

Create machine learning models

Duration

One day

Outline for Implementing a Machine Learning Solution with Microsoft Azure Databricks Training

Module 1: Introduction to Azure Databricks

In this module, you will learn how to provision an Azure Databricks workspace and cluster, and use them to work with data.

Lessons

Getting Started with Azure Databricks

Working with Data in Azure Databricks

Lab : Getting Started with Azure Databricks

Lab : Working with Data in Azure Databricks

After completing this module, you will be able to:

Provision an Azure Databricks workspace and cluster

Use Azure Databricks to work with data

Module 2: Training and Evaluating Machine Learning Models

In this module, you will learn how to use Azure Databricks to prepare data for modeling, and train and validate a machine learning model.

Lessons

Preparing Data for Machine Learning

Training a Machine Learning Model

Lab : Training a Machine Learning Model

Lab : Preparing Data for Machine Learning

After completing this module, you will be able to use Azure Databricks to:

Prepare data for modeling

Train and validate a machine learning model

Module 3: Managing Experiments and Models

In this module, you will learn how to use MLflow to track experiments running in Azure Databricks, and how to manage machine learning models.

Lessons

Using MLflow to Track Experiments

Managing Models

Lab : Using MLflow to Track Experiments

Lab : Managing Models

After completing this module, you will be able to:

Use MLflow to track experiments

Manage models

Module 4: Integrating Azure Databricks and Azure Machine Learning

In this module, you will learn how to integrate Azure Databricks with Azure Machine Learning

Lessons

Tracking Experiments with Azure Machine Learning

Deploying Models

Lab : Deploying Models in Azure Machine Learning

Lab : Running Experiments in Azure Machine Learning

After completing this module, you will be able to:

Run Azure Machine Learning experiments on Azure Databricks compute

Deploy models trained on Azure Databricks to Azure Machine Learning