We provide virtual course about DP-604: Implement a data science and machine learning solution for AI with Microsoft Fabric. This course provides participants with the knowledge and skills required to build data science and machine learning solutions using Microsoft Fabric.
Course description:
The focus is on understanding how to operationalize AI workloads, integrate machine learning models with data pipelines, and leverage Fabric’s analytics capabilities to deliver AI-powered insights. Machine learning and AI are essential components of modern data strategies. In this course, you’ll learn how Microsoft Fabric supports the full machine learning lifecycle - from data preparation and model training to deployment and monitoring.
Through hands-on labs and real-world examples, you will get to work with data transformation, feature engineering, ML models, and operationalizing AI solutions in an enterprise context. Participants will leave with practical experience that can be applied directly in business analytics and AI initiatives.
Key takeaways:
After completing this course, participants will be able to:
• Understand the machine learning workflow in Microsoft Fabric
• Prepare and explore data for machine learning
• Train and evaluate machine learning models
• Deploy models into production environments
• Integrate AI solutions with real-time and batch data pipelines
• Implement monitoring and governance for ML workloads
• Use Fabric tools for collaboration between data science and analytics teams
Course outline:
Module 1 - ML workflow in Microsoft Fabric:
• Participants explore the end-to-end machine learning lifecycle, including data ingestion, preprocessing, model training, and evaluation.
Module 2 - Data preparation and feature engineering:
• This section covers how to prepare data, clean and transform datasets, and engineer features that improve model performance.
Module 3 - Training and evaluating models:
• Participants learn how to build, train, and evaluate machine learning models using Fabric’s built-in tools and frameworks.
Module 4 - Model deployment:
• This section focuses on deploying models into production, including creating scoring endpoints and integrating models with applications or dashboards.
Module 5 - Integration with data pipelines:
• Learn how to connect machine learning models with real-time and batch data pipelines in Fabric to deliver up-to-date predictions and insights.
Module 6 - Monitoring and model governance:
• Participants gain experience in monitoring deployed models, managing retraining workflows, and applying governance best practices.
Module 7 - Collaboration and operationalization:
• This section explains how data science and analytics teams can collaborate using Fabric, including version control, reproducibility, and scalable workflows.
Target audience:
This course is intended for:
• Data Scientists
• Machine Learning Engineers
• Data Engineers
• BI Developers working with AI
• Analytics Architects
• IT professionals working with data and AI solutions
Prerequisites:
Recommended:
• Basic understanding of data analytics and machine learning concepts
• Experience with SQL and Python is beneficial
• Familiarity with Microsoft Fabric or other analytics platforms
Language:
• English course material, norwegian or english speaking instructor
Course material:
The course fee includes digital course documentation