Microsoft Future Ready: Designing and Implementing a Data Science Solution on Azure

Syllabus

• Week 1

  Course Introduction

  • About this Course

    This activity introduces you to the course outline and the learning outcomes. It also explains how to navigate the course on the FutureLearn platform and access the CloudSwyft Hands-On Labs.

  • Data Science

    In this activity, you will learn about data science and the different roles that comprise the data science team.

  • The Data Science Process

    In this activity, you will explore each of the predefined steps involved in the data science process.

  • Azure and Data Science

    In the activity, we will provide a broad overview of Azure services and products used for data science and machine learning.

  • Cloud-based and On-premises Machine Learning

    In this activity, you will examine the functionality and key features of Azure's cloud-based and on-premises machine learning products.

  • Wrapping up the Week

    This is the closing activity for Week 1, where you will have the opportunity to reflect on what you have learned during the week.

• Week 2

  Data Science on Azure

  • Development platforms and tools

    In this activity, we will review the development platforms and tools that Microsoft provides for machine learning.

  • Introduction to Data Science with Azure Notebooks

    In this activity, you will learn the functionality of Azure Notebooks through a number of exercises.

  • CloudSwyft Hands-On Lab 1

    In this activity, you will put the concepts from the previous activities into action by completing your first CloudSwyft Hands-On Lab.

  • Introducing the Azure Machine Learning (AML) Service

    This activity explains how the Azure Machine Learning service augments and automates the data science process through the use of containerisation and special machine learning services.

  • Training Machine Learning Models

    This activity demonstrates how to use Azure Machine Learning service to select a model, tune hyperparameters, train a model, deploy a model and monitor the deployed model.

  • Wrapping Up the Week

    This is the closing activity for Week 2, where you'll have the opportunity to reflect on what you've learned during the week.

• Week 3

  Azure Machine Learning Service

  • Registering and Deploying Models

    In this activity, you will create a deployment workspace, learn about registering a model to the container registry, and deploy a model as a web service.

  • CloudSwyft Hands-On Lab 2

    In this activity, you will complete your CloudSwyft Hands-On Lab to put the concepts from the previous activities into action.

  • Automating Model Selection with AML Service

    In this activity, you will learn about the machine learning pipeline and how AML service's AutoML can automate parts of it. You'll also learn about the Machine Learning Operations (MLOps) capabilities that AM Learning provides.

  • CloudSwyft Hands-On Lab 3

    In this activity, you will complete your CloudSwyft Hands-On Lab to put the concepts from the previous activities into action.

  • Managing and Monitoring Models with AML Service

    In this activity, you will learn about Azure Machine Learning service model management, and tracking features that can be integrated into pipelines.

  • Wrapping Up the Course

    This is the closing activity for Week 3 and the course where you'll have the opportunity to reflect on what you have learned!