Course Overview

The Microsoft Azure DP-100 training course from CloudThat is designed to train candidates who plan to take up the Microsoft DP-100 certification exam. Taking this course and passing the Microsoft DP-100 exam will meet all the requirements needed to become a Certified Microsoft Azure Data Scientist Associate. Candidates taking up this DP-100 certification course learn how to plan and create a suitable working environment for data science workloads on Azure. This Azure Data Scientist training also teaches candidates how to manage, optimize and deploy machine learning models into production and how to train predictive models and run data experiments.

After completing this course, students will be able to:

  • To understand and build AI solutions on Azure
  • To learn about various Azure Machine Learning services usage & integration
  • To understand the profound impacts Machine Learning is making in smart business decisions

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Key Features

  • Our Azure Data Scientist training modules have 50% -60% hands-on lab sessions to encourage Thinking-Based Learning (TBL).
  • Interactive-rich virtual and face-to-face classroom teaching to inculcate Problem-Based Learning (PBL).
  • Microsoft certified instructor-led training and mentoring sessions to develop Competency-Based Learning (CBL).
  • Well-structured use-cases to simulate challenges encountered in a Real-World environment.
  • Integrated teaching assistance and support through experts designed Learning Management System (LMS) and ExamReady platform.
  • Being a Microsoft Learning Partner provides us with the edge over competition.

Who Should Attend

  • Candidates serving as part of a multi-disciplinary team that incorporates ethical, privacy, and governance considerations into the solution.


  • Candidates typically have background in mathematics, statistics and computer science
  • Basic knowledge of Cloud platform: Azure
  • Basic understanding of Machine Learning
  • IT industry work experience or those pursuing a degree in the IT field
  • Strong learning acumen

Course Outline Download Course Outline

In this module, you will learn how to provision an Azure Machine Learning workspace and use it to manage machine learning assets such as data, compute, model training code, logged metrics, and trained models. You will learn how to use the web-based Azure Machine Learning studio interface as well as the Azure Machine Learning SDK and developer tools like Visual Studio Code and Jupyter Notebooks to work with the assets in your workspace.

Lessons :

  • Getting Started with Azure Machine Learning
  • Azure Machine Learning Tools


  • Creating an Azure Machine Learning Workspace
  • Working with Azure Machine Learning Tools

This module introduces the Designer tool, a drag and drop interface for creating machine learning models without writing any code. You will learn how to create a training pipeline that encapsulates data preparation and model training, and then convert that training pipeline to an inference pipeline that can be used to predict values from new data, before finally deploying the inference pipeline as a service for client applications to consume.


  • Training Models with Designer
  • Publishing Models with Designer


  • Creating a Training Pipeline with the Azure ML Designer
  • Deploying a Service with the Azure ML Designer

In this module, you will get started with experiments that encapsulate data processing and model training code, and use them to train machine learning models.


  • Introduction to Experiments
  • Training and Registering Models


  • Running Experiments
  • Training and Registering Models

Data is a fundamental element in any machine learning workload, so in this module, you will learn how to create and manage datastores and datasets in an Azure Machine Learning workspace, and how to use them in model training experiments.


  • Working with Datastores
  • Working with Datasets


  • Working with Datastores
  • Working with Datasets

One of the key benefits of the cloud is the ability to leverage compute resources on-demand, and use them to scale machine learning processes to an extent that would be infeasible on your own hardware. In this module, you’ll learn how to manage experiment environments that ensure consistent runtime consistency for experiments, and how to create and use compute targets for experiment runs.


  • Working with Environments
  • Working with Compute Targets


  • Working with Environments
  • Working with Compute Targets

Now that you understand the basics of running workloads as experiments that leverage data assets and compute resources, it’s time to learn how to orchestrate these workloads as pipelines of connected steps. Pipelines are key to implementing an effective Machine Learning Operationalization (ML Ops) solution in Azure, so you’ll explore how to define and run them in this module.


  • Introduction to Pipelines
  • Publishing and Running Pipelines


  • Creating a Pipeline
  • Publishing a Pipeline

Models are designed to help decision making through predictions, so they’re only useful when deployed and available for an application to consume. In this module learn how to deploy models for real-time inferencing, and for batch inferencing.


  • Real-time Inferencing
  • Batch Inferencing


  • Creating a Real-time Inferencing Service
  • Creating a Batch Inferencing Service

By this stage of the course, you’ve learned the end-to-end process for training, deploying, and consuming machine learning models; but how do you ensure your model produces the best predictive outputs for your data? In this module, you’ll explore how you can use hyperparameter tuning and automated machine learning to take advantage of cloud-scale compute and find the best model for your data.


  • Hyperparameter Tuning
  • Automated Machine Learning


  • Tuning Hyperparameters
  • Using Automated Machine Learning

Many of the decisions made by organizations and automated systems today are based on predictions made by machine learning models. It’s increasingly important to be able to understand the factors that influence the predictions made by a model, and to be able to determine any unintended biases in the model’s behavior. This module describes how you can interpret models to explain how feature importance determines their predictions.


  • Introduction to Model Interpretation
  • using Model Explainers


  • Reviewing Automated Machine Learning Explanations
  • Interpreting Models

After a model has been deployed, it’s important to understand how the model is being used in production, and to detect any degradation in its effectiveness due to data drift. This module describes techniques for monitoring models and their data.


  • Monitoring Models with Application Insights
  • Monitoring Data Drift


  • Monitoring a Model with Application Insights
  • Monitoring Data Drift


  • By earning DP-100 certification, you can be competent data scientist.
  • Display abilities to understand the profound impacts Machine Learning is making in smart business decisions.
  • On successful completion of Azure Data Scientist Associate course, aspirants receive a Course Completion Certificate from us.
  • By successfully clearing the DP-100 exam, aspirants earn Microsoft Certification.

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