DP-100T01: A Designing and Implementing a Data Science Solution on Azure

Instructor-led course to gain the skills needed to become a Microsoft Certified AI Engineer

What you will learn

  • Basics of Machine Learning
  • Basics of Data Science
  • Utilizing the Data Store
  • Working with datasets
  • Hands on Experience with Microsoft Azure
  • Running Experiments on Microsoft Azure
  • Utilizing no-Code Machine Learning
  • Modeling Interpretability in Azure ML
  • Orchestrating Machine Learning Workflows
  • Deploy and Consume Models
  • Training Optimal Models
  • Using Responsible AI


  • Experience with Python
  • Experience with PyTorch
  • Experience with TensorFlow
  • Experience with Scikit Learn
  • Students taking this course need internet speeds of at least 10 Mbps
  • At least two monitors are recommended with 1920 x1080 resolution


DP-100: Designing and Implementing a Data Science Solution on Azure is a Microsoft Certification course that helps you in understanding  wide range of Machine Learning models with their application in Data Science on the Azure Cloud. The students will learn to train and utilize different models using Azure Machine Learning Studio to execute machine learning workloads in the Azure Cloud. It helps in using no code Machine Learning Models as well as developing Custom Responsible Machine Learning Models.


The DP-100 certification course begins with the Introduction of Azure and Azure Machine Learning. After providing basic knowledge it provides a hands on experience with no-code machine learning using the Visual Tools. Moreover it helps you in training models and running experiments. The Average Salary of certified Microsoft Azure Data Scientist Associate (DP-100) in United States is $122,802.


Data is essential in Machine Learning Paradigm so the DP100 course guides the process of using Datastore and consuming the Datasets. Once the datasets are managed, compute instances are created to run the codes on the Azure Cloud.


Now that instances are ready the pipelines are managed and different models are trained and consumed for Decision Making. Once the Models are ready the Model training process is Optimized.


By the end of DP100 training course utilization of Responsible Machine Learning and monitoring the models for better performance is discussed.


 This course will help you become Microsoft Azure Data Scientist along with passing the Microsoft Certification exam.


The DP-100 roadmap comprises 10 modules; module 0 is the introductory module. In contrast, the rest of the modules are developed following the course requirements. Each module has 3-part lessons, Labs, and Quizzes.


So, if you are interested in deploying machine learning models or building machine learning solutions on the cloud, this exam DP 100 study guide is for you. Feel free to contact us anytime.


Prominent Features

  • Instructor-Led Course
  • Live Training Option
  • Flexible Deadlines
  • Self-Paced Learning
  • Shareable Certificate
  • Skillset Building To Get Job Ready
  • Scenario Based Labs
  • Earn a Career Credential
  • Licenses For Lab Completion
  • Enhanced Course Material
  • Helpdesk Support
  • Guidelines To Overcome Professional Challenges
  • Command Over Professional Expertise
  • Prepared Virtual Machines for Labs
  • Business Plan Option

Course Content
22 Lectures
10 Quizzes
20 Hands-on Labs
25 Hours
lessons and hands-on labs
  • Module 00: Course Introduction
    This module consists of a brief introduction of the Instructor along with the overall course introduction giving a heads up to the students in context of the course contents and all modules information.
    Lessons Duration
    Lesson 01: Introduction of Course
  • Module 01: Getting Started with Azure Machine Learning
    This session will teach you how to set up an Azure Machine Learning workspace and use it to manage machine learning assets including data, computation, model training code. To interact with the assets in your workspace and utilize the web-based Azure Machine Learning studio interface, as well as the Azure Machine Learning SDK and development tools such as Visual Studio Code and Jupyter Notebooks.
    Lessons Duration
    Lesson 01: Introduction to Azure Machine Learning
    Labs Duration
    Lab 1A: Create an Azure Machine Learning Workspace 32:04
    Lab 1B: Introduction to Azure ML SDK 32:31
  • Module 02: No Code Machine Learning 
    This module presents the Automated Machine Learning and Designer visual tools, which may be used to train, assess, and deploy machine learning models without writing any code.
    Lessons Duration
    Lesson 01: Automated Azure Machine Learning
    Lesson 02:
    Labs Duration
    Lab 2A: Use Automated Machine Learning 26:57
    Lab 2B: Deploying Inference Pipeline Using Azure ML Designer 42:28
  • Module 03: Running Experiments and Training Models
    In this module, you will begin by creating experiments that contain data processing and model training code, which you will then use to train machine learning models.
    Lessons Duration
    Lesson 01: Introduction to Experiments
    Lesson 02: Training and Registering Models
    Labs Duration
    Lab 3A: Creating Experiment 35:54
    Lab 3B: Training Models 46:16
  • Module 04: Working with Data
    Because data is an essential component of any machine learning workload, this module will teach you how to create and maintain datastores and datasets in an Azure Machine Learning workspace, as well as how to utilize them in model training trials.
    Lessons Duration
    Lesson 01: Working with Datastores
    Lesson 02: Working with Datasets
    Labs Duration
    Lab 4A: Working with Datastores 49:31
    Lab 4B: Working with Datasets 45:50
  • Module 05: Working With Compute
    This module will teach you how to design and use compute targets for experiment runs, as well as how to manage experiment settings that provide constant runtime consistency for experiments.
    Lessons Duration
    Lesson 01: Working with Environment
    Lesson 02: Working with Compute Targets
    Labs Duration
    Lab 5A: Working with Environment 51:02
    Lab 5B: Working with Compute 47:58
  • Module 06: Orchestrating Machine Learning Workflows
    Pipelines are essential for establishing a successful Machine Learning Operationalization (ML Ops) solution in Azure, thus this module will show you how to create and operate them
    Lessons Duration
    Lesson 01: Introduction to Pipelines
    Lesson 02: Publishing and Running Pipelines
    Labs Duration
    Lab 6A: Create a Pipeline 54:12
    Lab 6B: Publishing a Pipeline 34:59
  • Module 07: Deploying and Consuming Models
    Models are intended to aid decision making through predictions, hence they are only helpful when installed and ready for consumption by an application. Learn how to deploy models for both real-time and batch inference in this module.
    Lessons Duration
    Lesson 01: Real-Time Inferencing
    Lesson 02: Batch Inferencing
    Labs Duration
    Lab 7A: Creating a Real-time inference Service 39:43
    Lab 7B: Creating a Batch inference Service 47:48
  • Module 08: Training Optimal Models
    This module will show you how to use the Azure Machine Learning SDK to do hyperparameter tweaking and automated machine learning to identify the optimal model for your data.
    Lessons Duration
    Lesson 01: Hyper Parameter Tuning
    Lesson 02: Using Automated Machine Learning
    Labs Duration
    Lab 8A: Tuning Hyper Parameters 49:25
    Lab 8B: Using Automated Machine Learning from SDK 46:14
  • Module 09: Responsible Machine Learning
    This session looks at some of the considerations and approaches for implementing responsible machine learning concepts.
    Lessons Duration
    Lesson 01: Differential Privacy
    Lesson 02: Model Interpretability
    Lesson 03: Fairness
    Labs Duration
    Lab 9A: Reviewing Automated Machine Learning explanation 32:11
    Lab 9B: Interpret Models 44:22
  • Module 10: Monitoring Models
    After deploying a model, it is critical to evaluate how the model is being utilized in production and to detect any decline in its efficacy caused by data drift. This lesson explains how to monitor models and their data
    Lessons Duration
    Lesson 01: Monitoring Models with Application Insights
    Lesson 02: Monitoring Data Drift
    Labs Duration
    Lab 10A: Monitoring a Model 27:41
    Lab 10B: Monitoring Data Drift 25:04

About the Instructor

Muhammad Omer Aftab ( Azure Data Scientist )

Microsoft certified Azure Data Scientist Associate

Muhammad Omer Aftab is a Microsoft Certified Azure Data Scientist Associate. He holds a Bachelor of Science in Computer Sciences and a Master of Philosophy in Software Engineering. He is fascinated with Artificial Intelligence and its subfields, such as Machine Learning, Deep Learning, and Reinforcement Learning. He enjoys learning about it by devising practical applications of what he has learned. He believes that effort and passion, rather than information, is what motivates us to achieve our life's... Read More

Frequently Asked Questions

  • Does the DP-100T01-A course have hands-on labs?
    Yes, the DP-100T01 -A  course has hands-on practical labs for all the modules to practice different Data Science  processes in real lab environment; which are available 24 hours a day, for 30 days. 
  • What are the working hours for support team?
    Support team is available for assistance 8 hours a day (from Monday to Friday)
  • What is the meaning of ‘enhanced content’?
    Instructor Brandon's 'Enhanced Content' offers an exclusively designed course increase in the official Microsoft Curriculum, adding real-world changes that resonate with today's software updates.
  • Is there any demo available?
    Yes, demonstrations are available as reference, showing the steps to achieve solutions so that you do not get stuck during the hands-on component.
  • What is the refund policy for the course?
    There is no refund granted once the course is purchased as we provide all the course contents upon purchasing. All completed purchases are final and non-refundable. For more details, click here.
  • How long do I have access to the course?
    You have access to your purchased course for 2 months: 60 days for lectures and 30 days for labs.
  • Are there any special licenses being offered during training?
    We provide our students with the licenses required to do the course and the labs.
  • How do I extend lab time beyond 30 days?
    An ‘Extension Fee’ covers all the costs of your licenses and support, so for a fee of $400, you can extend your lab time. 
  • How many hours should I spent each week on the course?
    We suggest around 20 hours a week in order to complete this course on time.

DP-100T01: A Designing and Implementing a Data Science Solution on Azure

Instructor-led course to gain the skills needed to become a Microsoft Certified AI Engineer

    Prominent Features
  • 25 hours plus of course content
  • Virtual Machine for Labs
  • 22 Lectures, 20 Labs and 10 Quizzes
  • Dedicated support (8 hours a day)
  • Course completion certification