Course Overview
Build Machine Learning Solutions using Azure Databricks.
Azure Databricks is a fully managed, cloud-based data analytics platform, which empowers developers to accelerate AI and innovation by simplifying the process of building enterprise-grade data applications.
Built as a joint effort by Microsoft and the team that started Apache Spark, Azure Databricks provides data science, engineering, and analytical teams with a single platform for big data processing and machine learning.
In this course, you’ll learn how to use Azure Databricks to train and deploy machine learning models.
What are the skills covered
- Explore Azure Databricks
- Use Apache Spark in Azure Databricks
- Train a machine learning model in Azure Databricks
- Use MLflow in Azure Databricks
- Tune hyperparameters in Azure Databricks
- Use AutoML in Azure Databricks
- Train deep learning models in Azure Databricks
Who should attend this course
- IT Professionals
- AI Engineers
- Data Scientists
Course Curriculum
What are the Prerequisites
- Before starting this module, you should be familiar with machine learning on Azure Databricks.
Download Course Syllabus
Course Modules
Azure Databricks is a cloud service that provides a scalable platform for data analytics using Apache Spark.
Learning objectives
In this module, you’ll learn how to:
- Provision an Azure Databricks workspace.
- Identify core workloads and personas for Azure Databricks.
- Describe key concepts of an Azure Databricks solution.
Prerequisites
Before starting this module, you should have a fundamental knowledge of data analytics concepts. Consider completing Azure Data Fundamentals certification before starting this module.
Azure Databricks is built on Apache Spark and enables data engineers and analysts to run Spark jobs to transform, analyze and visualize data at scale.
Learning objectives
In this module, you’ll learn how to:
- Describe key elements of the Apache Spark architecture.
- Create and configure a Spark cluster.
- Describe use cases for Spark.
- Use Spark to process and analyze data stored in files.
- Use Spark to visualize data.
Prerequisites
Before starting this module, you should have a basic knowledge of Azure Databricks. Consider completing the Explore Azure Databricks module before this one.
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
In this module you’ll learn how to:
- Prepare data for machine learning
- Train a machine learning model
- Evaluate a machine learning model
Prerequisites
Before starting this module, you should be familiar with Azure Databricks. Consider completing the following modules first:
MLflow is an open source platform for managing the machine learning lifecycle that is natively supported in Azure Databricks.
Learning objectives
In this module, you’ll learn how to:
- Use MLflow to log parameters, metrics, and other details from experiment runs.
- Use MLflow to manage and deploy trained models.
Before starting this module, you should be familiar with Azure Databricks and the machine learning model training process.
Tuning hyperparameters is an essential part of machine learning. In Azure Databricks, you can use the Hyperopt library to optimize hyperparameters automatically.
Learning objectives
In this module, you will learn how to:
- Use the Hyperopt library to optimize hyperparameters.
- Distribute hyperparameter tuning across multiple worker nodes
Prerequisites
Before starting this module, you should be familiar with how to train machine learning models in Azure Databricks.
AutoML in Azure Databricks simplifies the process of building an effective machine learning model for your data.
Learning objectives
In this module, you’ll learn how to:
- Use the AutoML user interface in Azure Databricks
- Use the AutoML API in Azure Databricks
Prerequisites
Before starting this module, you should be familiar with machine learning in Azure Databricks.
Deep learning uses neural networks to train highly effective machine learning models for complex forecasting, computer vision, natural language processing, and other AI workloads.
Learning objectives
In this module, you’ll learn how to:
- Train a deep learning model in Azure Databricks
- Distribute deep learning training by using the Horovod library
Prerequisites
Before starting this module, you should be familiar with machine learning on Azure Databricks.
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Training Options
- ILT: Instructor-Led Training
- VILT: Virtual Instructor-Led Training
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Exam & Certification
Note: There is no exam directly associated with this course. However, Microsoft offers an extensive portfolio of industry-recognized certifications that can help you stand out as a tech professional in 2025 and beyond. Achieving Microsoft certification is one of the most effective ways to validate your skills and accelerate your career.
With our expert-led training, you’ll be prepared to:
- Master in-demand capabilities across Cloud, Data & AI, and Cybersecurity — areas driving global digital transformation.
- Prove your expertise with a globally respected credential recognized by employers worldwide.
- Advance your career by enhancing your credibility, increasing your earning potential, and opening doors to new opportunities.
Explore our full range of Microsoft certifications and start building the skills that matter today.
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