This course is part of the Data Science Fundamentals on Microsoft Azure ExpertTrack
Microsoft Future Ready: Designing and Implementing a Data Science Solution on Azure
Develop your understanding of how to design and implement a data science solution on Microsoft Azure.

- Duration3 weeks
- Weekly study6 hours
- 100% onlineTry this course for free
- Included in an ExpertTrackCourse 6 of 6
- Get full ExpertTrack access$39/month
Microsoft Future Ready: Designing and Implementing a Data Science Solution on Azure
This self-paced course was created by the Microsoft World Wide Learning team for data scientists, with the aim of supporting the exam DP-100: Designing and Implementing a Data Science Solution on Azure.
You’ll learn about the data science process and how solutions within Microsoft Azure can best support it. You’ll then delve into machine learning model training and deployment, allowing you to use a greater degree of automation within your data science projects.
Use the Azure machine learning to improve your data modelling
Machine learning is revolutionising a huge range of industries, including data science. Guided by industry experts, you’ll review the machine learning capabilities available in Microsoft Azure, and learn how to use it for local model training, model selection automation, model management, and monitoring.
Consolidate your knowledge by building a chatbot
Once you have built your understanding of Microsoft Azure and its functionality, you’ll put that knowledge into practice by building a simple chatbot. This offers you the opportunity to customise and troubleshoot within the platform, as well as a chance to build hands-on experience.
By the end of the course you’ll have the confidence and knowledge to design and implement a data science solution using Microsoft Azure’s AI stack.
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!
What will you achieve?
By the end of the course, you‘ll be able to...
- High level overview of Azure data science related services
- Deep dive on the premier data science service, Azure Machine Learning service, which supports automation of machine learning model training and deployment.
- Navigate the Azure portal and to create services there.
- Familiarity with the Azure data storage technologies
Who is the course for?
This course is best suited to aspiring data scientists and those whose job responsibilities primarily fall into the area of data science.
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