Overview
Azure Databricks is a cloud-scale platform for data analytics and machine learning. In this one-day DP-090T00: Implementing a Machine Learning Solution with Microsoft Azure Databricks 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.
Skills Covered
After completing this module, you 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
Who Should Attend
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.
Course Curriculum
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
Download Course Syllabus
Course Modules
Azure Databricks enables you to build highly scalable data processing and machine learning solutions.
Learning objectives
After completing this module, you will be able to:
- Understand Azure Databricks
- Provision Azure Databricks workspaces and clusters
- Work with notebooks in Azure Databricks
Prerequisites
Before starting this module, you should be familiar with the Microsoft Azure portal and provisioning Azure resources. You’ll get the most out of this module if you already have some experience of using Python in a notebook environment, such as Jupyter Notebooks.
To work with data in Azure Databricks, you can use the dataframe object.
Learning objectives
After completing this module, you will be able to:
- Understand dataframes
- Query dataframes
- Visualize data
Prerequisites
Before starting this module, you should have some experience of provisioning an Azure Databricks workspace and cluster.
Before using data to train a machine learning model, it’s important to prepare the data appropriately.
Learning objectives
After completing this module, you will be able to:
- Understand machine learning concepts
- Perform data cleaning
- Perform feature engineering
- Perform data scaling
- Perform data encoding
Prerequisites
Before starting this module, you should have some experience of using Python to train machine learning models using a framework such a Scikit-Learn.
Machine learning involves using data to train a predictive model. Azure Databricks support multiple commonly used machine learning frameworks that you can use to train models.
Learning objectives
After completing this module, you will be able to:
- Understand Spark ML
- Train and validate a model
- Use other machine learning frameworks
Prerequisites
Before starting this module, you should have some experience of using Python to train machine learning models using a framework such a Scikit-Learn.
When you run data science and machine learning experiments at scale, you can use MLflow to track experiment runs and metrics.
Learning objectives
After completing this module, you will be able to:
- Understand capabilities of MLflow
- Use MLflow terminology
- Run experiments
Prerequisites
Before starting this module, you should be familiar with using Python to train machine learning models in Azure Databricks.
In Azure Databricks, you can deploy and manage machine learning models that you have trained.
Learning objectives
After completing this module, you will be able to:
- Describe considerations for model management
- Register models
- Manage model versioning
Prerequisites
Before starting this module, you should be familiar with using Python to train machine learning models in Azure Databricks.
Azure Machine Learning is a scalable cloud platform for training, deploying, and managing machine learning solutions.
Learning objectives
After completing this module, you will be able to:
- Describe Azure Machine Learning
- Run Azure Databricks experiments in Azure Machine Learning
- Log metrics in Azure Machine Learning with MLflow
- Run Azure Machine Learning pipelines on Azure Databricks compute
Prerequisites
Before starting this module, you should be familiar with using Python to train machine learning models in Azure Databricks.
You can use Azure Databricks to train machine learning models, and deploy the trained models in Azure Machine Learning endpoints.
Learning objectives
After completing this module, you will be able to:
- Describe considerations for model deployment
- Plan for Azure Machine Learning deployment endpoints
- Deploy a model to Azure Machine Learning
- Troubleshoot model deployment
Prerequisites
Before starting this module, you should be familiar with using Python to train machine learning models in Azure Databricks.
Request More Information
Training Options
Exam & Certification
This course is not associated with any Certification. If you’d like to learn more about Azure certification roadmap, do check out our Microsoft Azure Training Certifications blog post.
Training & Certification Guide
Azure Strategy & Implementation Guide
Get a step-by-step introduction to using Azure for your cloud infrastructure with this Pack e-book. Read the latest edition of the Azure Strategy and Implementation Guide for detailed guidance on how to create a successful cloud adoption strategy with new innovations, capabilities, and security features from Microsoft Azure.
Microsoft Azure SQL Jumpstart Guide
Find out how to get started launching your first Azure SQL database or find ways to make your existing SQL database work harder. Download the Azure SQL Jumpstart Guide for detailed instructions and in-depth insights to help you make your Azure SQL deployment, migration, or enhancement run smoothly.
Low-code Application Development – Microsoft PowerApps and Azure
Build production-ready apps faster with a low-code environment. Quickly stand up your applications with Power Apps and get more time to apply your technical expertise to extending and optimizing those apps in Azure.
Azure Cloud Native Architecture Mapbook
Grow your cloud architecture skills with guidance from Azure Experts. Go beyond developing cloud-native applications to planning and implementing cloud application infrastructure. In this free e-book from Packt Publishing, you’ll find best practices for infrastructure design and patterns for building a complete solution.
Windows Virtual Desktop Security
Find out how to secure your Windows Virtual Desktop environment when migrating your virtual desktop infrastructure (VDI) to Azure. Read this security handbook to get technical hands-on guidance on how to help protect your apps and data in your Windows Virtual Desktop deployment.
Discover how to get more value from your on premises Windows Server and SQL Server investments and move some or all of your workloads to the cloud using your existing skills. See how to start using the cloud to support new ways of doing business and help ensure business continuity even if you need to keep some of your IT assets on-premises due to regulatory or data governance requirements.
Discover how to build highly scalable applications using containers and how to deploy and manage those containers at scale with Kubernetes on Azure. Read the completely reviewed and updated Packet e-book, Hands-On Kubernetes on Azure, Third Edition and discover what’s new, including security enhancements, continuous integration and continuous delivery (CI/CD) automation, and the latest supported technologies. Gain insight into building reliable applications in the new foreword by Kubernetes co-founder Brendan Burns.
Azure Synapse Analytics Proof of Concept Playbook
Learn how to perform a proof of concept efficiently and economically with Azure Synapse Analytics. Read the Azure Synapse Analytics Proof of Concept Playbook to understand the key concepts involved in deploying data warehousing, data lake, and big data workloads with Azure Synapse and get the evidence you need to make the case for implementation at your organization.
Spend less time managing server infrastructure and more time building great apps. Get your solutions to market faster using Azure Functions, a fully managed compute platform for processing data, integrating systems, and building simple APIs and microservices. The Azure Serverless Computing Cookbook will, through the development of basic back-end wep API that performs simple operations, helps you understand how to persist data in Azure Storage services.
Frequently Asked Questions
DP-050T00: Migrate SQL workloads to Azure
Learn about migrating SQL workloads to SQL Services on Azure in this 2-days Microsoft Official Course. Get guidance on how to migrate data platform technologies on Azure and how existing SQL based workloads can be migrated and modernized.
DP-060T00: Migrate NoSQL Workloads to Azure Cosmos DB
This DP-060T00: Migrate NoSQL Workloads to Azure Cosmos DB course will teach the students what is Cosmos DB and how you can migrate MongoDB and Cassandra workloads to Cosmos DB.
DP-070T00: Migrate Open Source Data Workloads to Azure
This course will enable the students to understand Azure SQL Database, and educate the students on what is required to migrate MySQL and PostgreSQL workloads to Azure SQL Database.
DP-080T00: Querying Data with Microsoft Transact-SQL Get started with Transact SQL
Learn the basics of Microsoft’s standard SQL language and master skills required as a data analyst, a data engineer, a data scientist, a database administrator or a database developer to query and modify data in relational databases that are hosted in Microsoft SQL Server-based database systems.
DP-100T01: Designing and Implementing a Data Science Solution on Azure
Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This DP-100T01: Designing and Implementing a Data Science Solution on Azure course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure.
DP-900T00: Microsoft Azure Data Fundamentals
In this course, students will learn the fundamentals of database concepts in a cloud environment, get basic skilling in cloud data services, and build their foundational knowledge of cloud data services within Microsoft Azure. Students will identify and describe core data concepts such as relational, non-relational, big data, and analytics, and explore how this technology is implemented with Microsoft Azure. They will explore the roles, tasks, and responsibilities in the world of data.
DP-203T00: Data Engineering on Microsoft Azure
In this DP-203T00 Data Engineering on Microsoft Azure course, the student will learn about the data engineering patterns and practices as it pertains to working with batch and real-time analytical solutions using Azure data platform technologies. Students will begin by understanding the core compute and storage technologies that are used to build an analytical solution.
DP-300T00: Administering Relational Databases on Microsoft Azure
This course provides students with the knowledge and skills to administer a SQL Server database infrastructure for cloud, on-premises and hybrid relational databases and who work with the Microsoft PaaS relational database offerings. Additionally, it will be of use to individuals who develop applications that deliver content from SQL-based relational databases.
DP-420T00: Designing and Implementing Cloud-Native Applications Using Microsoft Azure Cosmos DB
Unlock opportunities with Azure Cosmos DB.
This course teaches developers how to create application using the SQL API and SDK for Azure Cosmos DB. Students will learn how to write efficient queries, create indexing policies, manage and provisioned resources, and perform common operations with the SDK.
