Overview
Explore the data science process and learn how to train machine learning models to accomplish artificial intelligence in Microsoft Fabric.
Why Choose KORNERSTONE
- Accredited, practitioner-led, expert certificate trainers to provide high quality training
- Official training material included
- Practical, scenario-based learning – focus on application, not just theory
- Excellent Passing Rate
Skills Covered
- Get started with data science in Microsoft Fabric
- Explore data for data science with notebooks in Microsoft Fabric
- Preprocess data with Data Wrangler in Microsoft Fabric
- Train and track machine learning models with MLflow in Microsoft Fabric
- Generate batch predictions using a deployed model in Microsoft Fabric
Prerequisites
- You should be familiar with basic data concepts and terminology, principles of machine learning,
Target Audience
- Data Scientist
- Data Analyst
- Data Engineer
Course Curriculum
Module 1: Get started with data science in Microsoft Fabric
In Microsoft Fabric, data scientists can manage data, notebooks, experiments, and models while easily accessing data from across the organization and collaborating with their fellow data professionals.
Learning objectives
In this module, you’ll learn how to:
- Understand the data science process
- Train models with notebooks in Microsoft Fabric
- Track model training metrics with MLflow and experiments
Module 2: Explore data for data science with notebooks in Microsoft Fabric
Microsoft Fabric notebooks serve as a comprehensive tool for data exploration, enabling users to uncover hidden patterns and relationships in their datasets.
Learning objectives
In this module, you’ll:
- Load data and perform initial data exploration.
- Gain knowledge about different types of data distributions.
- Understand the concept of missing data, and strategies to handle missing data effectively.
- Visualize data using various data visualization techniques and libraries.
Module 3: Preprocess data with Data Wrangler in Microsoft Fabric
Data Wrangler serves as a comprehensive tool for preprocessing data. It enables users to clean data, handle missing values, and transform features to build machine learning models.
Learning objectives:
In this module, you’ll:
- Learn Data Wrangler features, and its role in the data science workflow.
- Perform different types of preprocessing operations in data science.
- Learn how to handle missing values, and imputation strategies.
- Use one-hot encoding and other techniques to convert categorical data into a format suitable for machine learning algorithms.
Module 4: Train and track machine learning models with MLflow in Microsoft Fabric
In Microsoft Fabric, data scientists can train models in notebooks, track their work in experiments, and manage their models with MLflow.
Learning objectives:
In this module, you’ll learn how to:
- Train machine learning models with open-source frameworks
- Train models with notebooks in Microsoft Fabric
- Track model training metrics with MLflow and experiments in Microsoft Fabric
Module 5: Generate batch predictions using a deployed model in Microsoft Fabric
Save and use your machine learning models in Microsoft Fabric to generate batch predictions and enrich your data.
Learning objectives:
In this module, you’ll learn how to:
- Save a model in the Microsoft Fabric workspace
- Prepare a dataset for batch predictions
- Apply the model to dataset to generate new predictions
- Save the predictions to a Delta table
Dates & Locations
October 21, 2026
Exam & Certification
To earn this Microsoft Applied Skills credential, learners demonstrate the ability to implement a data science solution by using Microsoft Fabric, including:
- Ingesting, loading, exploring, and preparing data
- Training, tracking, and scoring a model
Candidates for this credential should be familiar with data science and AI fundamentals, in addition to open-source frameworks, such as scikit-learn and SynapseML. They should also have experience with:
- Python
- MLflow
- Synapse Data Science in Microsoft Fabric
