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

Course Content

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

• Lesson 1: Getting Started with Azure Machine Learning

  In this lesson, 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.

  • Introduction to Azure Machine Learning
  • Working with Azure Machine Learning
  • Lab : Create an Azure Machine Learning Workspace

  
  
  

  After completing this lesson you will be able to:

  • Provision an Azure Machine Learning workspace
  • Use tools and code to work with Azure Machine Learning
• Lesson 2: Visual Tools for Machine Learning

  This lesson introduces the Automated Machine Learning and Designer visual tools, which you can use to train, evaluate, and deploy machine learning models without writing any code.

  • Automated Machine Learning
  • Azure Machine Learning Designer
  • Lab : Use Automated Machine Learning
  • Lab : Use Azure Machine Learning Designer

  
  
  

  After completing this lesson you will be able to:

  • Use automated machine learning to train a machine learning model
  • Use Azure Machine Learning designer to train a model
• Lesson 3: Running Experiments and Training Models

  In this lesson, 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 : Train Models
  • Lab : Run Experiments

  
  
  

  After completing this lesson you will be able to:

  • Run code-based experiments in an Azure Machine Learning workspace
  • Train and register machine learning models
• Lesson 4: Working with Data

  Data is a fundamental element in any machine learning workload, so in this lesson, 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
  • Lab : Work with Data

  
  
  

  After completing this lesson you will be able to:

  • Create and use datastores
  • Create and use datasets
• Lesson 5: Working with Compute

  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 lesson, 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 : Work with Compute

  
  
  

  After completing this lesson you will be able to:

  • Create and use environments
  • Create and use compute targets
• Lesson 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 lesson.

  • Introduction to Pipelines
  • Publishing and Running Pipelines
  • Lab : Create a Pipeline

  
  
  

  After completing this lesson you will be able to:

  • Create pipelines to automate machine learning workflows
  • Publish and run pipeline services
• Lesson 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 lesson learn how to deploy models for real-time inferencing, and for batch inferencing.

  • Real-time Inferencing
  • Batch Inferencing
  • Continuous Integration and Delivery
  • Lab : Create a Real-time Inferencing Service
  • Lab : Create a Batch Inferencing Service

  
  
  

  After completing this lesson you will be able to:

  • Publish a model as a real-time inference service
  • Publish a model as a batch inference service
  • Describe techniques to implement continuous integration and delivery
• Lesson 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 lesson, 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 : Use Automated Machine Learning from the SDK
  • Lab : Tune Hyperparameters

  
  
  

  After completing this lesson you will be able to:

  • Optimize hyperparameters for model training
  • Use automated machine learning to find the optimal model for your data
• Lesson 9: Responsible Machine Learning

  Data scientists have a duty to ensure they analyze data and train machine learning models responsibly; respecting individual privacy, mitigating bias, and ensuring transparency. This lesson explores some considerations and techniques for applying responsible machine learning principles.

  • Differential Privacy
  • Model Interpretability
  • Fairness
  • Lab : Explore Differential provacy
  • Lab : Interpret Models
  • Lab : Detect and Mitigate Unfairness

  
  
  

  After completing this lesson you will be able to:

  • Apply differential provacy to data analysis
  • Use explainers to interpret machine learning models
  • Evaluate models for fairness
• Lesson 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 lesson describes techniques for monitoring models and their data.

  • Monitoring Models with Application Insights
  • Monitoring Data Drift
  • Lab : Monitor Data Drift
  • Lab : Monitor a Model with Application Insights

  
  
  

  After completing this lesson you will be able to:

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

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