- Home
- IT Courses
- MS-DP100T01: Designing and Implementing a Data Science Solution on Azure
MS-DP100T01: Designing and Implementing a Data Science Solution on Azure
Course Code: MS-DP100T01
Gain the necessary knowledge about how to use Azure services to develop, train, and deploy, machine learning solutions. The course starts with an overview of Azure services that support data science. From there, it focuses on using Azure's premier data science service, Azure Machine Learning service, to automate the data science pipeline. This course is focused on Azure and does not teach the student how to do data science. It is assumed students already know that.
This course is aimed at data scientists and those with significant responsibilities in training and deploying machine learning models.
Before attending this course, students must have:
- Azure Fundamentals
- Understanding of data science including how to prepare data, train models, and evaluate competing models to select the best one.
- How to program in the Python programming language and use the Python libraries: pandas, scikit-learn, matplotlib, and seaborn.
After completing this course, students will be able to:
- Understand the data science in Azure
- Use Machine Learning to automate the end to end process
- Manage and monitor the Machine Learning service
This course will prepare delegates to write the Microsoft DP-100: Designing and Implementing a Data Science Solution on Azure exam.
Modules
Learn how to design a data ingestion solution for training data used in machine learning projects.
Lessons
- Introduction
- Identify your data source and format
- Choose how to serve data to machine learning workflows
- Design a data ingestion solution
- Exercise: Design a data ingestion strategy
- Knowledge check
- Summary
By the end of this module, you'll be able to:
- Identify your data source and format.
- Choose how to serve data to machine learning workflows.
- Design a data ingestion solution.
Learn how to design a model training solution for machine learning projects.
Lessons
- Introduction
- Identify machine learning tasks
- Choose a service to train a machine learning model
- Decide between compute options
- Exercise: Design a model training strategy
- Knowledge check
- Summary
By the end of this module, you'll be able to:
- Identify machine learning tasks
- Choose a service to train a model
- Choose between compute options
Learn how to design a model deployment solution and how the requirements of the deployed model can affect the way you train a model.
Lessons
- Introduction
- Understand how model will be consumed
- Decide on real-time or batch deployment
- Exercise - Design a deployment solution
- Knowledge check
- Summary
By the end of this module, you'll be able to:
- Understand how a model will be consumed.
- Decide whether to deploy your model to a real-time or batch endpoint.
Learn about machine learning operations or MLOps to bring a model from development to production. Identify options for monitoring and retraining when preparing a model for production.
Lessons
- Introduction
- Explore an MLOps architecture
- Design for monitoring
- Design for retraining
- Knowledge check
- Summary
By the end of this module, you'll be able to:
- Explore an MLOps architecture.
- Design for monitoring.
- Design for retraining.
As a data scientist, you can use Azure Machine Learning to train and manage your machine learning models. Learn what Azure Machine Learning is and get familiar with all its resources and assets.
Lessons
- Introduction
- Create an Azure Machine Learning workspace
- Identify Azure Machine Learning resources
- Identify Azure Machine Learning assets
- Train models in the workspace
- Exercise - Explore the workspace
- Knowledge check
- Summary
By the end of this module, you'll be able to:
- Create an Azure Machine Learning workspace.
- Identify resources and assets.
- Train models in the workspace.
Learn how you can interact with the Azure Machine Learning workspace. You can use the Azure Machine Learning studio, the Python SDK (v2), or the Azure CLI (v2).
Lessons
- Introduction
- Explore the studio
- Explore the Python SDK
- Explore the CLI
- Exercise - Explore the developer tools
- Knowledge check
- Summary
By the end of this module, you'll be able to:
- The Azure Machine Learning studio.
- The Python Software Development Kit (SDK).
- The Azure Command Line Interface (CLI).
Learn about how to connect to data from the Azure Machine Learning workspace. You're introduced to datastores and data assets.
Lessons
- Introduction
- Understand URIs
- Create a datastore
- Create a data asset
- Exercise - Make data available in Azure Machine Learning
- Knowledge check
- Summary
By the end of this module, you'll be able to:
- Access data by using Uniform Resource Identifiers (URIs).
- Connect to cloud data sources with datastores.
- Use data asset to access specific files or folders.
Learn how to work with compute targets in Azure Machine Learning. Compute targets allow you to run your machine learning workloads. Explore how and when you can use a compute instance or compute cluster.
Lessons
- Introduction
- Choose the appropriate compute target
- Create and use a compute instance
- Create and use a compute cluster
- Exercise - Work with compute resources
- Knowledge check
- Summary
By the end of this module, you'll be able to:
- Choose the appropriate compute target.
- Work with compute instances and clusters.
- Manage installed packages with environments.
Learn how to use environments in Azure Machine Learning to run scripts on any compute target.
Lessons
- Introduction
- Understand environments
- Explore and use curated environments
- Create and use custom environments
- Exercise - Work with environments
- Knowledge check
- Summary
By the end of this module, you'll be able to:
- Understand environments in Azure Machine Learning.
- Explore and use curated environments.
- Create and use custom environments.
Learn how to convert your code to a script and run it as a command job in Azure Machine Learning.
Lessons
- Introduction
- Preprocess data and configure featurization
- Run an Automated Machine Learning experiment
- Evaluate and compare models
- Exercise - Find the best classification model with Automated Machine Learning
- Knowledge check
- Summary
By the end of this module, you'll be able to:
- Prepare your data to use AutoML for classification.
- Configure and run an AutoML experiment.
- Evaluate and compare models.
Learn how to use MLflow for model tracking when experimenting in notebooks.
Lessons
- Introduction
- Configure MLflow for model tracking in notebooks
- Train and track models in notebooks
- Exercise - Track model training
- Knowledge check
- Summary
By the end of this module, you'll be able to:
- Configure to use MLflow in notebooks
- Use MLflow for model tracking in notebooks
Learn how to convert your code to a script and run it as a command job in Azure Machine Learning.
Lessons
- Introduction
- Convert a notebook to a script
- Run a script as a command job
- Use parameters in a command job
- Exercise - Run a training script as a command job
- Knowledge check
- Summary
By the end of this module, you'll be able to:
- Convert a notebook to a script.
- Test scripts in a terminal.
- Run a script as a command job.
- Use parameters in a command job.
Learn how to track model training with MLflow in jobs when running scripts.
Lessons
- Introduction
- Track metrics with MLflow
- View metrics and evaluate models
- Exercise - Use MLflow to track training jobs
- Knowledge check
- Summary
By the end of this module, you'll be able to:
- Use MLflow when you run a script as a job.
- Review metrics, parameters, artifacts, and models from a run.
Learn how to perform hyperparameter tuning with a sweep job in Azure Machine Learning.
Lessons
- Introduction
- Define a search space
- Configure a sampling method
- Configure early termination
- Use a sweep job for hyperparameter tuning
- Exercise - Run a sweep job
- Knowledge check
- Summary
By the end of this module, you'll be able to:
- Define a hyperparameter search space.
- Configure hyperparameter sampling.
- Select an early-termination policy.
- Run a sweep job.
Learn how to create and use components to build pipeline in Azure Machine Learning. Run and schedule Azure Machine Learning pipelines to automate machine learning workflows.
Lessons
- Introduction
- Create components
- Create a pipeline
- Run a pipeline job
- Exercise - Run a pipeline job
- Knowledge check
- Summary
By the end of this module, you'll be able to:
- Create components.
- Build an Azure Machine Learning pipeline.
- Run an Azure Machine Learning pipeline.
Learn how to log and register an MLflow model in Azure Machine Learning.
Lessons
- Introduction
- Log models with MLflow
- Understand the MLflow model format
- Register an MLflow model
- Exercise - Log and register models with MLflow
- Knowledge check
- Summary
By the end of this module, you'll be able to:
- Log models with MLflow.
- Understand the MLmodel format.
- Register an MLflow model in Azure Machine Learning.
Explore model explanations, error analysis, counterfactuals, and causal analysis by creating a Responsible AI dashboard. You'll create and run the pipeline in Azure Machine Learning using the Python SDK v2 to generate the dashboard.
Lessons
- Introduction
- Understand Responsible AI
- Create the Responsible AI dashboard
- Evaluate the Responsible AI dashboard
- Exercise - Explore the Responsible AI dashboard
- Knowledge check
- Summary
By the end of this module, you'll be able to:
- Understand Azure Machine Learning's built-in components for responsible AI.
- Create a Responsible AI dashboard.
- Explore a Responsible AI dashboard.
Learn how to deploy models to a managed online endpoint for real-time inferencing.
Lessons
- Introduction
- Explore managed online endpoints
- Deploy your MLflow model to a managed online endpoint
- Deploy a model to a managed online endpoint
- Test managed online endpoints
- Exercise - Deploy an MLflow model to an online endpoint
- Knowledge check
- Summary
By the end of this module, you'll be able to:
- Use managed online endpoints.
- Deploy your MLflow model to a managed online endpoint.
- Deploy a custom model to a managed online endpoint.
- Test online endpoints.
Learn how to deploy models to a batch endpoint. When you invoke a batch endpoint, you'll trigger a batch scoring job.
Learning objectives
In this module, you'll learn how to:
- Introduction
- Understand and create batch endpoints
- Deploy your MLflow model to a batch endpoint
- Deploy a custom model to a batch endpoint
- Invoke and troubleshoot batch endpoints
- Exercise - Deploy an MLflow model to a batch endpoint
- Knowledge check
- Summary
By the end of this module, you'll be able to:
- Create a batch endpoint.
- Deploy your MLflow model to a batch endpoint.
- Deploy a custom model to a batch endpoint.
- Invoke batch endpoints