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DP 604 Implement a data science and machine learning solution for AI in Microsoft Fabric

Duration: 1 Day
Course Code: DP 604
Course Delivery: Virtual (1-Day) GMT/Virtual (1-Day) PST
Level: Beginner

Course Overview

Explore the data science process and learn how to train machine learning models to accomplish artificial intelligence in Microsoft Fabric.

Audience Profile

Preferably, the audience should already be familiar with the data science process, Python and open-source frameworks like scikit-learn to train machine learning models. 

Course Objectives

After completing this course, students will be able to:

• Load data into a Lakehouse 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.

Course Outline

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.

Lessons

Introduction
Understand the data science process
Explore and process data with Microsoft Fabric
Train and score models with Microsoft Fabric
Exercise - Explore data science in Microsoft Fabric

After completing this module, students will be able 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+

Learn how Microsoft Fabric notebooks serve as a comprehensive tool for data exploration, enabling users to uncover hidden patterns and relationships in their datasets.

Lessons

Introduction
Explore notebooks
Load data for exploration
Understand data distribution
Check for missing data in notebooks
Apply advanced data exploration techniques
Visualize charts in notebooks
Exercise: Use notebook for data exploration in Microsoft Fabric

After completing this module, students will be able to:

 
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.

Lessons

Introduction
Understand Data Wrangler
Perform data exploration
Handle missing data
Transform data with operators
Exercise: Preprocess data with Data Wrangler in Microsoft Fabric

After completing this module, students will be able to:

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.


Lessons

• Introduction
• Understand how to train machine learning models
• Train and track models with MLflow and experiments
• Manage models in Microsoft Fabric
• Exercise - Train and track a model in Microsoft Fabric

After completing this module, students will be able 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.

Lessons

Introduction
Customize the model's behavior for batch scoring
Prepare data before generating predictions
Generate and save predictions to a Delta table
Exercise - Generate and save batch predictions

After completing this module, students will be able 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

Book this Course

Date Delivery Format Location Price Add to cart
30 May 2024 Virtual (1-Day) PST Online £195.00 £125.00 Book me in
13 June 2024 Virtual (1-Day) GMT Online £195.00 £125.00 Book me in
4 July 2024 Virtual (1-Day) PST Online £195.00 £125.00 Book me in
18 July 2024 Virtual (1-Day) GMT Online £195.00 £125.00 Book me in

If you have any questions or are looking for customised training for you or your team, please contact us

Exam Information