Hybrid Immersion: DP-100 - Designing and Implementing a Data Science Solution on Azure Course Outline

Special Note to New Hampshire Residents
This course has not yet been approved by the New Hampshire Department of Education. Please contact us for an update on when the class will be available in New Hampshire.

Course Overview
Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Azure Machine Learning and MLflow.

Why Choose ONLC's Hybrid Immersion Classes?

Accelerated Learning: Our unique blend of live instructor-led sessions and on-demand training allows you to master AI concepts quickly and efficiently.

Flexibility: Enjoy the benefits of both in-person instruction and self-paced learning, giving you the freedom to learn at your own pace.

Free Second-Shot Exam Voucher: If you don't pass your certification exam on the first attempt using the Microsoft-provided voucher, we'll give you a second chance at no additional cost.

How Our Hybrid Immersion Classes Work

Kick-Off Session: We start with an orientation to familiarize you with course content, exam preparation, and the learning management system.

Condensed Instructor-Led Training: Live sessions with an expert instructor are typically half the duration of regular classes, allowing you to grasp key concepts quickly.

On-Demand Learning: Access Microsoft Official content through our learning management system to reinforce your understanding and practice at your own pace.

Free Second-Shot Exam: If you don't pass the exam on your first attempt, simply share a screenshot of your exam score, and we'll provide you with a second-shot exam voucher.

Audience profile
This course is aimed at data scientists and those with significant responsibilities in training and deploying machine learning models.

Prerequisites
Before attending this course, students must have:
• Azure Fundamentals Understanding of data science including how to prepare data, train models, and evaluate competing models to select the best one.
• Python programming ability and use the Python libraries: pandas, scikit-learn, matplotlib, and seaborn.

Instructor-led Content Note
Because this Hybrid Immersion class combines instructor-led and on-demand training, every topic in this outline will not be covered by the instructor. Learners will need to review the remaining topics through ONLC's Learning Management System which includes links to the complete Microsoft Official Curriculum.

Course Outline

1 - Design a data ingestion strategy for machine learning projects
Identify your data source and format
Choose how to serve data to machine learning workflows
Design a data ingestion solution

2 - Design a machine learning model training solution
Identify machine learning tasks
Choose a service to train a machine learning model
Decide between compute options

3 - Design a model deployment solution
Understand how model will be consumed
Decide on real-time or batch deployment

4 - Design a machine learning operations solution
Explore an MLOps architecture
Design for monitoring
Design for retraining

5 - Explore Azure Machine Learning workspace resources and assets
Create an Azure Machine Learning workspace
Identify Azure Machine Learning resources
Identify Azure Machine Learning assets
Train models in the workspace

6 - Explore developer tools for workspace interaction
Explore the studio
Explore the Python SDK
Explore the CLI

7 - Make data available in Azure Machine Learning
Understand URIs
Create a datastore
Create a data asset

8 - Work with compute targets in Azure Machine Learning
Choose the appropriate compute target
Create and use a compute instance
Create and use a compute cluster

9 - Work with environments in Azure Machine Learning
Understand environments
Explore and use curated environments
Create and use custom environments

10 - Find the best classification model with Automated Machine Learning
Preprocess data and configure featurization
Run an Automated Machine Learning experiment
Evaluate and compare models

11 - Track model training in Jupyter notebooks with MLflow
Configure MLflow for model tracking in notebooks
Train and track models in notebooks

12 - Run a training script as a command job in Azure Machine Learning
Convert a notebook to a script
Run a script as a command job
Use parameters in a command job

13 - Track model training with MLflow in jobs
Track metrics with MLflow
View metrics and evaluate models

14 - Perform hyperparameter tuning with Azure Machine Learning
Define a search space
Configure a sampling method
Configure early termination
Use a sweep job for hyperparameter tuning

15 - Run pipelines in Azure Machine Learning
Create components
Create a pipeline
Run a pipeline job

16 - Register an MLflow model in Azure Machine Learning
Log models with MLflow
Understand the MLflow model format
Register an MLflow model

17 - Create and explore the Responsible AI dashboard for a model in Azure Machine Learning
Understand Responsible AI
Create the Responsible AI dashboard
Evaluate the Responsible AI dashboard

18 - Deploy a model to a managed online endpoint
Explore managed online endpoints
Deploy your MLflow model to a managed online endpoint
Deploy a model to a managed online endpoint
Test managed online endpoints

19 - Deploy a model to a batch endpoint
Understand and create batch endpoints
Deploy your MLflow model to a batch endpoint
Deploy a custom model to a batch endpoint
Invoke and troubleshoot batch endpoints
View outline in Word

ADP10H

Attend hands-on, instructor-led Hybrid Immersion: DP-100 - Designing and Implementing a Data Science Solution on Azure training classes at ONLC's more than 300 locations. Not near one of our locations? Attend these same live classes from your home/office PC via our Remote Classroom Instruction (RCI) technology.

For additional training options, check out our list of Azure Courses and select the one that's right for you.