ICT 915 - Designing and Implementing a Data Science Solution on Azure

Course Description

In this course, you will learn how to apply data science and machine learning to implement and run machine learning workloads on Azure, which include planning and creating a suitable working environment for data science workloads, running data experiments and train predictive models, as well as managing, optimizing and deploying machine learning models into production.

This course covers the objectives for Microsoft Exam DP-100: Designing and Implementing a Data Science Solution on Azure. This exam is required to fulfil the requirements of the Microsoft Certified: Azure Data Scientist Associate professional certification. Discounted exam vouchers for DP-100 will be available for purchase for all learners (optional).

Course Details

This course is funded by the Microsoft Canada Skills initiative. It is delivered in collaboration with Microsoft Canada.

Learning Outcomes

By completion of this course, successful students will be able to:

• Create an Azure Machine Learning workspace, and manage compute, data, and coding environments for machine learning workloads
• Use Azure Machine Learning for “no-code” machine learning model training and deployment.
• Create and run experiments that log metrics and train machine learning models.
• Create and manage datastores and datasets, and use data in machine learning experiments.
• Create and manage compute resources, and use them to run machine learning experiments at scale in the cloud.
• Use Pipelines to orchestrate machine learning operations.
• Deploy predictive models as real-time or batch inference services, and consume them from client applications.
• Find the optimal model for your data by using hyperparameter tuning and automated machine learning.
• Apply principles and techniques that support responsible machine learning practices.
• Monitor usage and data drift for deployed models.

Topics

Module 1: Getting Started with Azure Machine Learning

In this module, 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.

Module 2: Visual Tools for Machine Learning

This module 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.

Module 3: Running Experiments and Training Models

In this module, you will get started with experiments that encapsulate data processing and model training code, and use them to train machine learning models.

Module 4: Working with Data

Data is a fundamental element in any machine learning workload, so in this module, 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.

Module 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 module, 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.

Module 6: Orchestrating Machine Learning Workflows

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 module.

Module 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 module learn how to deploy models for real-time inferencing, and for batch inferencing.

Module 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 module, you'll explore how you can use the azure Machine Learning SDK to apply hyperparameter tuning and automated machine learning, and find the best model for your data.

Module 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 module explores some considerations and techniques for applying responsible machine learning principles.

Module 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 module describes techniques for monitoring models and their data.

Who is this course for?

Individuals who are keen to learn how to use Azure Machine Learning to operate machine learning workloads in the cloud. This course is designed for those who wish to pursue future careers like:

• Data Scientist
• Data Science Developer
• Data Science Specialist / Analyst
• Data Science Consultant

Notes

This course includes extensive hands-on activities designed to help you learn by working with Azure Machine Learning. To complete the labs in this course, you will need:

• A modern web browser - for example, Microsoft Edge
• The lab files for this course, which are published online at https://aka.ms/mslearn-dp100
• Microsoft Azure Pass for Lab (will be provided at no additional cost from Section 3 onwards)