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Learn how to train machine learning models in notebooks and track your work with MLflow experiments in Microsoft Fabric.

Units in this learning path:

• Introduction
• Train Model
• Mlflow
• Fabric Models
• Exercise
• Knowledge Check
• Summary

Products: Fabric

Roles: Data Analyst, Data Engineer, Data Scientist

Level: Beginner

Subjects: Data Management, Machine Learning

Duration: 54 minutes

Learn how to use deep learning libraries like PyTorch in Azure Databricks, and to distribute training by using TorchDistributor

Units in this learning path:

• Introduction
• Deep Learning
• Pytorch
• Horovod
• Exercise Deep Learning
• Knowledge Check
• Summary

Products: Azure Databricks

Roles: Data Scientist

Level: Advanced

Subjects: Data Management, Machine Learning

Duration: 70 minutes

Learn how to transform and load event log data for process mining.

Units in this learning path:

• Introduction
• Normalize Event Log Data
• Filter Event Log Data
• Load Event Log Data
• Exercise
• Check
• Summary

Products: Power Automate

Roles: Functional Consultant, Developer

Level: Beginner

Subjects: Data Management

Duration: 43 minutes

Apply low-code transformations using Power Query in Dataflows Gen2 to prepare analytical data for downstream consumption.

Units in this learning path:

• Introduction
• Understand Dataflows
• Transform Power Query
• Optimize Performance
• Exercise
• Knowledge Check
• Summary

Products: Fabric

Roles: Data Analyst, Data Engineer

Level: Intermediate

Subjects: Data Analytics, Data Engineering

Duration: 61 minutes

Use T-SQL in Microsoft Fabric warehouses to transform and query data, create reusable views and stored procedures, and build dimensional tables.

Units in this learning path:

• Introduction
• Transform Queries
• Create Views
• Build Stored Procedures
• Implement Dimensional Tables
• Exercise
• Knowledge Check
• Summary

Products: Fabric

Roles: Data Analyst, Data Engineer

Level: Intermediate

Subjects: Data Analytics, Data Engineering

Duration: 84 minutes

Use Fabric notebooks to transform data with Spark SQL and PySpark, connecting to lakehouses, warehouses, and other data stores.

Units in this learning path:

• Introduction
• Describe Notebooks
• Shape Clean Data
• Combine Aggregate
• Write Delta Tables
• Exercise
• Knowledge Check
• Summary

Products: Fabric

Roles: Data Analyst, Data Engineer

Level: Intermediate

Subjects: Data Analytics, Data Engineering

Duration: 68 minutes

Try Azure Cosmos DB for NoSQL

Units in this learning path:

• Introduction
• Explore Resources
• Review Basic Operations
• Exercise Create Account
• Knowledge Check
• Summary

Products: Azure Cosmos Db

Roles: Developer

Level: Intermediate

Subjects: Databases

Duration: 32 minutes

Learn how to use the Optuna library in Azure Databricks to tune machine learning hyperparameters.

Units in this learning path:

• Introduction
• Hyperopt
• Review Hyperopt Trials
• Scale Hyperopt
• Exercise Use Mlflow
• Knowledge Check
• Summary

Products: Azure Databricks

Roles: Data Scientist

Level: Intermediate

Subjects: Data Management, Machine Learning

Duration: 68 minutes

Relational databases store many different types of data for many different types of applications. Some databases have thousands of users and some have just one user. Some tables have tens of rows while others have millions of rows. Some data is unique while other data is duplicated. All of these variables mean that database management systems (DBMSs) need systems to understand the data and the queries that are running against this data to ensure optimum performance. When you perform tune a database, it's important to understand how performance tuning works and how you can assess performance metrics.

Units in this learning path:

• Introduction
• Understand Statistics
• Assess Query Performance Query Store
• Exercise Assess Query Performance Query Store
• Knowledge Check
• Summary

Products: Azure Database Postgresql

Roles: Database Administrator

Level: Intermediate

Subjects: Databases

Duration: 31 minutes

Learn how Azure Databricks integrates with Microsoft tools and services including Fabric, Power BI, VS Code, Power Platform, Copilot Studio, Purview, and Foundry to enable comprehensive data and AI solutions.

Units in this learning path:

• Introduction
• Integration Databricks Microsoft Fabric
• Integration Databricks Power Bi
• Integration Databricks Vscode
• Integration Databricks Power Platform
• Integration Databricks Copilot Studio
• Integration Databricks Microsoft Purview
• Integration Databricks Microsoft Foundry
• Knowledge Check
• Summary

Products: Azure Databricks

Roles: Data Engineer

Level: Intermediate

Subjects: Data Engineering

Duration: 69 minutes

Learn about Azure Databricks architecture, including the account hierarchy, control and compute planes, and different storage options for managing data with Unity Catalog.

Units in this learning path:

• Introduction
• Understand Azure Databricks Architecture
• Understand Unity Catalog Managed Storage
• Understand External Storage
• Understand Default Storage
• Knowledge Check
• Summary

Products: Azure Databricks

Roles: Data Engineer

Level: Intermediate

Subjects: Data Engineering

Duration: 38 minutes

Learn the fundamentals of Microsoft Fabric IQ, including how ontologies create a shared business vocabulary, the components that make up the Fabric IQ ecosystem, and how ontology modeling differs from traditional data modeling approaches.

Units in this learning path:

• Introduction
• Get Started with Fabric Iq
• Explore Fabric Iq Components
• Understand Ontology Modeling Paradigm
• Knowledge Check
• Summary

Products: Fabric

Roles: Data Analyst

Level: Beginner

Subjects: Data Analytics

Duration: 37 minutes