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

Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning 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
  • Create and configure Azure Machine Learning workspaces, resources, datastores, data assets, and compute targets.
  • Train, track, and tune machine learning models using notebooks, MLflow, Automated Machine Learning, and hyperparameter sweep jobs.
  • Build pipelines and register models to automate and organize machine learning workflows.
  • Evaluate models with the Responsible AI dashboard to ensure fairness, transparency, and reliability.
  • Deploy models to managed online and batch endpoints and validate their performance.
  • Use Azure AI Foundry to select, deploy, and optimize models, develop RAG-based solutions, and implement responsible generative AI practices.
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. This class uses official Microsoft courseware and will be delivered by a Microsoft Certified Trainer (MCT).

Course Outline
  1. 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
  2. Explore developer tools for workspace interaction
    1. Explore the studio
    2. Explore the Python SDK
    3. Explore the CLI
  3. Make data available in Azure Machine Learning
    1. Understand URIs
    2. Create a datastore
    3. Create a data asset
  4. 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
  5. Work with environments in Azure Machine Learning
    1. Understand environments
    2. Explore and use curated environments
    3. Create and use custom environments
  6. 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
  7. Track model training in Jupyter notebooks with MLflow
    1. Configure MLflow for model tracking in notebooks
    2. Train and track models in notebooks
  8. 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
  9. Track model training with MLflow in jobs
    1. Track metrics with MLflow
    2. View metrics and evaluate models
  10. 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
  11. Run pipelines in Azure Machine Learning
    1. Create components
    2. Create a pipeline
    3. Run a pipeline job
  12. Register an MLflow model in Azure Machine Learning
    1. Log models with MLflow
    2. Understand the MLflow model format
    3. Register an MLflow model
  13. 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
  14. 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
  15. 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
  16. Plan and prepare to develop AI solutions on Azure
    1. What is AI?
    2. Azure AI services
    3. Azure AI Foundry
    4. Developer tools and SDKs
    5. Responsible AI
  17. Choose and deploy models from the model catalog in Azure AI Foundry portal
    1. Explore the model catalog
    2. Deploy a model to an endpoint
    3. Optimize model performance
  18. Develop an AI app with the Azure AI Foundry SDK
    1. What is the Azure AI Foundry SDK?
    2. Work with project connections
    3. Create a chat client
  19. Get started with prompt flow to develop language model apps in the Azure AI Foundry
    1. Understand the development lifecycle of a large language model (LLM) app
    2. Understand core components and explore flow types
    3. Explore connections and runtimes
    4. Explore variants and monitoring options
  20. Develop a RAG-based solution with your own data using Azure AI Foundry
    1. Understand how to ground your language model
    2. Make your data searchable
    3. Create a RAG-based client application
    4. Implement RAG in a prompt flow
  21. Fine-tune a language model with Azure AI Foundry
    1. Understand when to fine-tune a language model
    2. Prepare your data to fine-tune a chat completion model
    3. Explore fine-tuning language models in Azure AI Foundry portal
  22. Implement a responsible generative AI solution in Azure AI Foundry
    1. Plan a responsible generative AI solution
    2. Map potential harms
    3. Measure potential harms
    4. Mitigate potential harms
    5. Manage a responsible generative AI solution
  23. Evaluate generative AI performance in Azure AI Foundry portal
    1. Assess the model performance
    2. Manually evaluate the performance of a model
    3. Automated evaluations
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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