DP-090: Implementing a Machine Learning Solution with Microsoft Azure Databricks

About this course

Azure Databricks is a cloud-scale platform for data analytics and machine learning. In this one-day course, you'll learn how to use Azure Databricks to explore, prepare, and model data; and integrate Databricks machine learning processes with Azure Machine Learning.

Audience profile

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.

At course completion

After completing this course, students will be able to:

• 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

Course Outline

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

Before attending this course, you should have experience of using Python to work with data, and some knowledge of machine learning concepts.