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Microsoft Certified: Azure Data Scientist Associate (DP100)

In this three-day course, students will 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 in Microsoft Azure.

This training is a comprehensive preparation for the DP-100: Designing and Implementing a Data Science Solution on Azure exam to earn the Microsoft Certified: Azure Data Scientist Associate certification.

Microsoft

A preferential rate (-15%) applies to the regular cost for non-profit organizations, as well as the government sector. In addition, you can benefit from additional advantages through a corporate agreement when you need to train several people or teams in your company. Contact us for details.

Public class

Virtual classroom
Tentative dateTentative date
May 1 2023
$1895$
 
English
Register now
Virtual classroom
Tentative dateTentative date
June 12 2023
$1895$
 
English
Register now
Virtual classroom
Tentative dateTentative date
July 24 2023
$1895$
 
English
Register now
Virtual classroom
Tentative dateTentative date
September 18 2023
$1895$
 
English
Register now
1895$
Duration: 
3 days / 21 hours

Private class

Virtual classroom
Minimum no. of participants: 5
3 days / 21 hours
Price on request
English or French
Contact us
Training plan: 

Designing and Implementing a Data Science Solution on Azure (DP-100T01)

Module 1: Introduction to the Azure Machine Learning SDK

  • Azure Machine Learning workspaces
  • Azure Machine Learning tools and interfaces
  • Azure Machine Learning experiments

Module 2: Use Automated Machine Learning in Azure Machine Learning

  • What is machine learning?
  • What is Azure Machine Learning studio?
  • What is Azure Automated Machine Learning?
  • Understand the AutoML process

Module 3: Create a classification model with Azure Machine Learning designer

  • Identify classification machine learning scenarios
  • What is Azure Machine Learning?
  • What is Azure Machine Learning designer?
  • Understand steps for classification

Module 4: Train a machine learning model with Azure Machine Learning

  • Run a training script
  • Using script parameters
  • Registering models

Module 5: Work with Data in Azure Machine Learning

  • Introduction to datastores
  • Use datastores
  • Introduction to datasets
  • Use datasets

Module 6: Work with Compute in Azure Machine Learning

  • Introduction to environments
  • Introduction to compute targets
  • Create compute targets
  • Use compute targets

Module 7: Orchestrate machine learning with pipelines

  • Introduction to pipelines
  • Pass data between pipeline steps
  • Reuse pipeline steps
  • Publish pipelines
  • Use pipeline parameters
  • Schedule pipelines

Module 8: Deploy real-time machine learning services with Azure Machine Learning

  • Deploy a model as a real-time service
  • Consume a real-time inferencing service
  • Troubleshoot service deployment

Module 9: Deploy batch inference pipelines with Azure Machine Learning

  • Creating a batch inference pipeline
  • Publishing a batch inference pipeline

Module 10: Tune hyperparameters with Azure Machine Learning

  • Defining a search space
  • Configuring sampling
  • Configuring early termination
  • Running a hyperparameter tuning experiment

Module 11: Automate machine learning model selection with Azure Machine Learning

  • Automated machine learning tasks and algorithms
  • Preprocessing and featurization
  • Running automated machine learning experiments

Module 12: Explore differential privacy

  • Understand differential privacy
  • Configure data privacy parameters

Module 13: Explain machine learning models with Azure Machine Learning

  • Feature importance
  • Using explainers
  • Creating explanations
  • Visualizing explanations

Module 14: Detect and mitigate unfairness in models with Azure Machine Learning

  • Consider model fairness
  • Analyze model fairness with Fairlearn
  • Mitigate unfairness with Fairlearn

Module 15: Monitor data drift with Azure Machine Learning

  • Creating a data drift monitor
  • Scheduling alerts

Module 16: Monitor models with Azure Machine Learning

  • Enable Application Insights
  • Capture and view telemetry
Exclusives: 
  • One year access to the class recording
  • 180 days access to the lab environment after class
  • One voucher to take the exam
  • Up to date courseware with Microsoft Learn
  • One year subscription to the All Access Pass library containing hundreds of complementary practice labs
  • Microsoft course achievement badge
View the complete list of exclusives offered for your course
Prerequisites: 

Prior to attending this training, students should have:

  • earned the Microsoft Certified: Azure Data Fundamentals (DP900) certification or equivalent knowledge.
  • an understanding of data science, including data preparation, model training, and evaluating competing models to choose the best one.
  • an understanding of data science with Python; including how to prepare data, and train machine learning models using common machine learning libraries such as Scikit-Learn, PyTorch, or Tensorflow.
Certification information: 

Exam characteristics:

  • Exam code: DP-100
  • Cost: 0$ (included in your training)
  • Skills measured
    • Manage Azure resources for machine learning 
    • Run experiments and train models 
    • Deploy and operationalize machine learning solutions 
    • Implement responsible machine learning 
  • All details... 
Follow-on trainings: 
Microsoft Certified: Azure Data Engineer Associate (DP203)
Microsoft Certified: Administering Microsoft Azure SQL Solutions (DP300)
Audiences: 
Data Specialist
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Eccentrix
Office: 1-888-718-9732
E-mail: info@eccentrix.ca

130, King Street West, Suite 1800
Toronto, Ontario M5X 1E3
www.eccentrix.ca