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DP-604 Implement a data science and machine learning solution for AI with Microsoft Fabric
Machine learning and data science are at the heart of modern AI applications. Combining both in a unified platform shortens development cycles and gets models into production faster.
Key topics
- Build and manage data science workflows in Microsoft Fabric.
- Design, train, and evaluate ML models from the ground up.
- Configure analytics architectures specifically for AI scenarios.
- Automate data pipelines end-to-end – from source to prediction.
- Operate, monitor, and continuously improve AI solutions.
Prerequisite
A background in data analysis and machine learning is required. Familiarity with cloud architectures makes getting started easier.
Target group
This training is designed for people who work with data and AI projects and want to implement them effectively using Microsoft Fabric.
Microsoft Fabric brings all essential tools together in one consistent environment. You build hands-on skills you can apply directly in your day-to-day work.
Course content
Getting started with data science in Microsoft Fabric
- Learning and applying the end-to-end data science process.
- Exploring datasets and processing them within Fabric.
- Building and assessing machine learning models in Fabric.
Exploratory data analysis in Microsoft Fabric
- Understanding the structure and purpose of notebooks.
- Preparing and staging datasets for analysis.
- Investigating statistical distributions in datasets.
- Detecting and handling missing values in notebooks.
- Using more sophisticated analytical techniques effectively.
- Creating and interpreting visual data representations in notebooks.
Data cleansing with the Data Wrangler
- How the Data Wrangler works and where to use it.
- Reviewing and assessing raw data.
- Identifying and filling gaps in datasets.
- Applying transformation operators to reshape data.
Model management with MLflow in Fabric
- Building and training ML models step by step.
- Tracking model experiments systematically with MLflow.
- Storing and managing trained models within Fabric.
Scalable batch predictions in Fabric
- Configuring ML models for batch scoring workflows.
- Structuring and preparing input data for predictions.
- Persisting prediction results in a delta table.
Why is Microsoft Fabric so relevant for data science right now?
- Analytics and AI are merging in modern platforms. Microsoft Fabric brings both under one roof and makes ML projects significantly more efficient.
What typical challenges does Microsoft Fabric address in ML projects?
- Fragmented data, slow pipelines, and poor scalability are replaced by integrated workflows and faster model deployment.
What knowledge helps ensure a smooth start?
- Those familiar with Python, data analysis, and the basics of machine learning can quickly move into real-world scenarios and apply what they’ve learned right away.
How hands-on is the machine learning training in Fabric?
- The focus is on real projects: data preparation, model training, and deployment all happen within a single, consistent platform.
What makes Microsoft Fabric stand out from traditional ML toolkits?
- Unified data storage, seamless tool integration, and fewer interface issues deliver greater efficiency across the entire ML lifecycle.
What career prospects does Fabric expertise open up?
- The combination of data, AI, and cloud skills is in high demand. Those who master Fabric are well positioned for modern data and AI roles.
What happens to businesses without solid machine learning knowledge?
- Untapped data potential, delayed decision-making, and lost competitive advantages are typical consequences of missing ML capabilities.
What outcomes can well-implemented Fabric projects realistically deliver?
- Better decision-making, productive ML models, and faster insights – made possible by integrated data and AI workflows on a single platform.
