Designing and Implementing a Data Science Solution on Azure

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

Course Length: 4 days
Delivery Methods: Multiple delivery options
Course Overview

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 using Azure Machine Learning. Participants will learn to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Azure Machine Learning and MLflow.

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.

Course Benefits
  • Learn about the data science process and the role of the data scientist.
  • Understand how Azure services can support and augment the data science process.
  • Learn to use Azure Machine Learning service to automate the data science process end to end.
  • Learn about the machine learning pipeline and how the Azure Machine Learning service's AutoML and HyperDrive can automate some of the laborious parts of it.
  • Learn how to automatically manage and monitor machine learning models in the Azure Machine Learning service.
Available Delivery Methods
Public Class
Public expert-led online training from the convenience of your home, office or anywhere with an internet connection. Guaranteed to run .
Private Class
Private classes are delivered for groups at your offices or a location of your choice.
Microsoft Certified Partner

Webucator is a Microsoft Certified Partner for Learning Solutions (CPLS). This class uses official Microsoft courseware and will be delivered by a Microsoft Certified Trainer (MCT).

Course Outline
  1. Design a data ingestion strategy for machine learning projects
    1. Identify your data source and format
    2. Choose how to serve data to machine learning workflows
    3. Design a data ingestion solution
  2. Design a machine learning model training solution
    1. Identify machine learning tasks
    2. Choose a service to train a machine learning model
    3. Decide between compute options
  3. Design a model deployment solution
    1. Understand how model will be consumed
    2. Decide on real-time or batch deployment
  4. Design a machine learning operations solution
    1. Explore an MLOps architecture
    2. Design for monitoring
    3. Design for retraining
  5. Explore Azure Machine Learning workspace resources and assets
    1. Create an Azure Machine Learning workspace
    2. Identify Azure Machine Learning resources
    3. Identify Azure Machine Learning assets
    4. Train models in the workspace
  6. Explore developer tools for workspace interaction
    1. Explore the studio
    2. Explore the Python SDK
    3. Explore the CLI
  7. Make data available in Azure Machine Learning
    1. Understand URIs
    2. Create a datastore
    3. Create a data asset
  8. Work with compute targets in Azure Machine Learning
    1. Choose the appropriate compute target
    2. Create and use a compute instance
    3. Create and use a compute cluster
  9. Work with environments in Azure Machine Learning
    1. Understand environments
    2. Explore and use curated environments
    3. Create and use custom environments
  10. Find the best classification model with Automated Machine Learning
    1. Preprocess data and configure featurization
    2. Run an Automated Machine Learning experiment
    3. Evaluate and compare models
  11. Track model training in Jupyter notebooks with MLflow
    1. Configure MLflow for model tracking in notebooks
    2. Train and track models in notebooks
  12. Run a training script as a command job in Azure Machine Learning
    1. Convert a notebook to a script
    2. Run a script as a command job
    3. Use parameters in a command job
  13. Track model training with MLflow in jobs
    1. Track metrics with MLflow
    2. View metrics and evaluate models
  14. Perform hyperparameter tuning with Azure Machine Learning
    1. Define a search space
    2. Configure a sampling method
    3. Configure early termination
    4. Use a sweep job for hyperparameter tuning
  15. Run pipelines in Azure Machine Learning
    1. Create components
    2. Create a pipeline
    3. Run a pipeline job
  16. Register an MLflow model in Azure Machine Learning
    1. Log models with MLflow
    2. Understand the MLflow model format
    3. Register an MLflow model
  17. Create and explore the Responsible AI dashboard for a model in Azure Machine Learning
    1. Understand Responsible AI
    2. Create the Responsible AI dashboard
    3. Evaluate the Responsible AI dashboard
  18. Deploy a model to a managed online endpoint
    1. Explore managed online endpoints
    2. Deploy your MLflow model to a managed online endpoint
    3. Deploy a model to a managed online endpoint
    4. Test managed online endpoints
  19. Deploy a model to a batch endpoint
    1. Understand and create batch endpoints
    2. Deploy your MLflow model to a batch endpoint
    3. Deploy a custom model to a batch endpoint
    4. Invoke and troubleshoot batch endpoints
Class Materials

Each student will receive a comprehensive set of materials, including course notes and all the class examples.

Class Prerequisites

Experience in the following is required for this Azure class:

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. Before taking this course, you should have experience:

  • Creating cloud resources in Microsoft Azure.
  • Using Python to explore and visualize data.
  • Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow.
  • Working with containers.
Prerequisite Courses

Courses that can help you meet these prerequisites:

Live Public Class
$2,445.10 / student
Please select a class.
Live Private Class
  • Private Class for your Team
  • Live training
  • Online or On-location
  • Customizable
  • Expert Instructors
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