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


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Upcoming Dates

Nov 1 - Nov 3, 2021
09:00 - 17:00

Dec 6 - Dec 8, 2021
09:00 - 17:00

Jan 10 - Jan 12, 2022
09:00 - 17:00

  Feb 22 - Feb 24, 2022
09:00 - 17:00

  • Course DP-100T01-A: Designing and Implementing a Data Science Solution on Azure
    3 days  (Instructor Led Online)  |  Azure Data Scientist

    Course Details

    The Designing and Implementing a Data Science Solution on Azure (DP-100T01-A) course provides the skills to planning and creating a suitable working environment for data science workloads on Azure, running data experiments and training predictive models, managing and optimizing models, and deploying machine learning models into production.

    In the DP-100T01-A course, the Azure Data Scientist will apply their knowledge of data science and machine learning to implement and run machine learning workloads on Azure; in particular, using Azure Machine Learning Service.


    See other Microsoft courses


    Module 1: Introduction to Azure Machine Learning

    The DP-100T01-A course starts by teaching 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.


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

    Lab: Creating an Azure Machine Learning Workspace

    Lab: Working with Azure Machine Learning Tools

    After completing this module, you will be able to

    • Provision an Azure Machine Learning workspace
    • Use tools and code to work with Azure Machine Learning

    Module 2: No-Code Machine Learning with Designer

    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

    Lab: Creating a Training Pipeline with the Azure ML Designer

    Lab: Deploying a Service with the Azure ML Designer

    After completing this module, you will be able to

    • Use the designer to train a machine learning model
    • Deploy a Designer pipeline as a service

    Module 3: Running Experiments and Training Models

    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

    Lab: Running Experiments

    Lab: Training and Registering Models

    After completing this module, you will be able to

    • Run code-based experiments in an Azure Machine Learning workspace
    • Train and register machine learning models

    Module 4: Working with Data

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


    • Working with Datastores
    • Working with Datasets

    Lab: Working with Datastores

    Lab: Working with Datasets

    After completing this module, you will be able to

    • Create and consume datastores
    • Create and consume datasets

    Module 5: Compute Contexts

    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

    Lab: Working with Environments

    Lab: Working with Compute Targets

    After completing this module, you will be able to

    • Create and use environments
    • Create and use compute targets

    Module 6: Orchestrating Operations with Pipelines

    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

    Lab: Creating a Pipeline

    Lab: Publishing a Pipeline

    After completing this module, you will be able to

    • Create pipelines to automate machine learning workflows
    • Publish and run pipeline services

    Module 7: Deploying and Consuming Models

    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

    Lab: Creating a Real-time Inferencing Service

    Lab: Creating a Batch Inferencing Service

    After completing this module, you will be able to

    • Publish a model as a real-time inference service
    • Publish a model as a batch inference service

    Module 8: Training Optimal Models

    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

    Lab: Tuning Hyperparameters

    Lab: Using Automated Machine Learning

    After completing this module, you will be able to

    • Optimize hyperparameters for model training
    • Use automated machine learning to find the optimal model for your data

    Module 9: Interpreting Models

    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 behaviour. This module describes how you can interpret models to explain how feature importance determines their predictions.


    • Introduction to Model Interpretation
    • using Model Explainers

    Lab: Reviewing Automated Machine Learning Explanations

    Lab: Interpreting Models

    After completing this module, you will be able to

    • Generate model explanations with automated machine learning
    • Use explainers to interpret machine learning models

    Module 10: Monitoring 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

    Lab: Monitoring a Model with Application Insights

    Lab: Monitoring Data Drift

    After completing this module, you will be able to

    • Use Application Insights to monitor a published model
    • Monitor data drift

    Target Audience

    The DP-100T01-A 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.


    Before attending the DP-100T01-A course, students must have:

    • A fundamental knowledge of Microsoft Azure.
    • Experience of writing Python code to work with data, using libraries such as Numpy, Pandas, and Matplotlib.
    • Understanding of data science; including how to prepare data, and train machine learning models using common machine learning libraries such as Scikit-Learn, PyTorch, or Tensorflow.