DP-100: Designing and Implementing a Data Science Solution on Azure

Welcome to DP-100 course! Designing and Implementing a Data Science Solution on Azure (DP-100) course is designed to provide learners with the skills necessary to design, build, deploy, and maintain machine learning and advanced analytics solutions on Azure. With this course, you will learn the fundamentals of working with Azure Machine Learning, Azure Databricks, and related data science services on the Microsoft Azure platform. You will design, develop, test, deploy, monitor and secure end-to-end cloud data science solutions.

DP-100 course is applicable to both technical and non-technical audiences and provides learners with a comprehensive understanding of the Azure platform and its features. You will also gain an understanding of the different types of services available for data science development on Azure. This course covers topics such as big data analysis with distributed computing, streaming analytics and machine learning to deliver insights from large datasets. It also covers the implementation of security and compliance.

This course will build on these topics with hands-on lab learnings and Knowledge Check questions.

Microsoft Courseware

Instructor-Led Training

Course Duration: 3-Days (24-Hour)

Microsoft Official Lab Exercises

Courseware Life Time Free Upgrade

Cloud Lab Access


This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.Successful Azure Data Scientists start this role with a fundamental knowledge of cloud computing concepts, and experience in general data science and machine learning tools and techniques.

The DP-100T01: Designing and Implementing a Data Science Solution on Azure Training is an ideal training for data professionals, engineers, scientists and architects looking to broaden their skills in creating quality data science solutions on the Microsoft Azure platform. The course provides a comprehensive overview of the various data science solutions and capabilities of the Azure platform

This course is ideal for those wishing to learn more about Azure services for data science projects, such as data engineers, data architects, data analysts, and application developers. Also, this is suited for those who have some knowledge of analytics fundamentals and wish to learn more about using Azure services to create advanced analytics models and solutions.


Module 1: Getting Started with Azure Machine Learning

  • Introduction to Azure Machine Learning
  • Working with Azure Machine Learning

Module 2: Visual Tools for Machine Learning

  • Automated Machine Learning
  • Azure Machine Learning Designer

Module 3: Running Experiments and Training Models

  • Introduction to Experiments
  • Training and Registering Models

Module 4: Working with Data

  • Working with Datastores
  • Working with Datasets

Module 5: Working with Compute

  • Environments
  • Compute Targets

Module 6: Orchestrating Machine Learning Workflows

  • Introduction to Pipelines
  • Publishing and Running Pipelines

Module 7: Deploying and Consuming Models

  • Real-time Inferencing
  • Batch Inferencing
  • Continuous Integration and Delivery

Module 8: Training Optimal Models

  • Hyperparameter Tuning
  • Automated Machine Learning

Module 9: Responsible Machine Learning

  • Differential Privacy
  • Model Interpretability
  • Fairness

Module 10: Monitoring Models

  • Monitoring Models with Application Insights
  • Monitoring Data Drift

Fees And Schedule

Instructor-Led Training

24-Hour of Instructor-Led Training One to one doubt resolution sessions Microsoft Official Lab Access

Learning Objectives

Learn how to use Azure Machine Learning to operate machine learning workloads in the cloud:

  • Build on your existing data science and machine learning knowledge
  • Leverage cloud services to perform machine learning at scale
  • Explore considerations for responsible machine learning
  • Able to use the Azure Machine Learning Model Management Service to manage and deploy predictive models.


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