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

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.
   1. Introduction to Azure Machine Learning
   2. Working with Azure Machine Learning
   3. On completing this lesson, you will have learned to:
      • Automated Machine Learning
      • Azure Machine Learning Designer
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.
   1. Automated Machine Learning
   2. Azure Machine Learning Designer
   3. On completing this lesson, you will have learned to:
      • Introduction to Experiments
      • Training and Registering Models
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.
   1. Introduction to Experiments
   2. Training and Registering Models
   3. On completing this lesson, you will have learned to:
      • Working with Datastores
      • Working with Datasets
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.
   1. Working with Datastores
   2. Working with Datasets
   3. On completing this lesson, you will have learned to:
      • Working with Environments
      • Working with Compute Targets
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.
   1. Working with Environments
   2. Working with Compute Targets
   3. On completing this lesson, you will have learned to:
      • Introduction to Pipelines
      • Publishing and Running Pipelines
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.
   1. Introduction to Pipelines
   2. Publishing and Running Pipelines
   3. On completing this lesson, you will have learned to:
      • Real-time Inferencing
      • Batch Inferencing
      • Continuous Integration and Delivery
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.
   1. Real-time Inferencing
   2. Batch Inferencing
   3. Continuous Integration and Delivery
   4. On completing this lesson, you will have learned to:
      • Hyperparameter Tuning
      • Automated Machine Learning
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.
   1. Hyperparameter Tuning
   2. Automated Machine Learning
   3. On completing this lesson, you will have learned to:
      • Differential Privacy
      • Model Interpretability
      • Fairness
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.
   1. Differential Privacy
   2. Model Interpretability
   3. Fairness
   4. On completing this lesson, you will have learned to:
      • Monitoring Models with Application Insights
      • Monitoring Data Drift
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.
    1. Monitoring Models with Application Insights
    2. Monitoring Data Drift

Course Labs

1. Create an Azure Machine Learning Workspace
2. Use Automated Machine Learning
3. Use Azure Machine Learning Designer
4. Train Models
5. Run Experiments
6. Work with Data
7. Work with Compute
8. Create a Pipeline
9. Create a Real-time Inferencing Service
10. Create a Batch Inferencing Service
11. Use Automated Machine Learning from the SDK
12. Tune Hyperparameters
13. Explore Differential privacy
14. Interpret Models
15. Detect and Mitigate Unfairness
16. Monitor Data Drift
17. Monitor a Model with Application Insights