
DP-100T01 - Designing and Implementing a Data Science Solution on Azure
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 in Microsoft Azure.
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.
- 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.
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).
- 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
- On completing this lesson, you will have learned to:
- Automated Machine Learning
- Azure Machine Learning Designer
- 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
- On completing this lesson, you will have learned to:
- Introduction to Experiments
- Training and Registering Models
- 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
- On completing this lesson, you will have learned to:
- Working with Datastores
- Working with Datasets
- 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
- On completing this lesson, you will have learned to:
- Working with Environments
- Working with Compute Targets
- 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
- On completing this lesson, you will have learned to:
- Introduction to Pipelines
- Publishing and Running Pipelines
- 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
- On completing this lesson, you will have learned to:
- Real-time Inferencing
- Batch Inferencing
- Continuous Integration and Delivery
- 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
- On completing this lesson, you will have learned to:
- Hyperparameter Tuning
- Automated Machine Learning
- 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
- On completing this lesson, you will have learned to:
- Differential Privacy
- Model Interpretability
- Fairness
- 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
- On completing this lesson, you will have learned to:
- Monitoring Models with Application Insights
- Monitoring Data Drift
- 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
Course Labs
- Create an Azure Machine Learning Workspace
- Use Automated Machine Learning
- Use Azure Machine Learning Designer
- Train Models
- Run Experiments
- Work with Data
- Work with Compute
- Create a Pipeline
- Create a Real-time Inferencing Service
- Create a Batch Inferencing Service
- Use Automated Machine Learning from the SDK
- Tune Hyperparameters
- Explore Differential privacy
- Interpret Models
- Detect and Mitigate Unfairness
- Monitor Data Drift
- Monitor a Model with Application Insights
Each student will receive a comprehensive set of materials, including course notes and all the class examples.
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.
Request a Private Class
- Private Class for your Team
- Online or On-location
- Customizable
- Expert Instructors