Azure Tutorials 2020

In November 2020, the Harvard Data Science Initiative is piloting a new tutorial series on using Microsoft Azure. Pre-recorded tutorials and in-person online sessions will be offered by experts from Microsoft for the Harvard community.

This training series is intended for a broad audience including students, researchers, faculty, post-docs, staff and anyone interested in getting started and learning Azure.


This is a series of Azure applied workshops where participants will start with the basics of cloud, getting started on Azure, and will then move on to learn about Azure’s Data and Machine Learning tools through a tailored series of online sessions and by interacting directly with the instructors in live workshop sessions and office hours.

This training series is intended for a broad audience including students, researchers, faculty, post-docs, staff and anyone interested in getting started and learning Azure.

This series will get you started on Azure and provide hands on experience managing data and leveraging Azure’s data science and machine learning tools.

The Azure workshop series will focus on the following topics:

  1. Azure 101: Getting Started.
  2. Azure Data Management and Storage.
  3. Azure Machine Learning.

Participants may register for any or all of the sessions. Participants must complete Session 1: Azure 101 or have equivalent knowledge in order to participate in Sessions 2 and 3.


This new virtual Azure training series uses a flipped classroom model.  The tutorials are pre-recorded by the instructors and made available via a link in advance.  Students can watch videos on their own time and at their own pace.

Live workshop office hours will be associated with each topic: Azure 101, Azure Data, and Azure ML.  Students will have the chance to interact with the instructors, ask questions, review specific issues, and walk through demos/examples.


Session 1.  Azure 101: Cloud basics and Getting Started (required* for Sessions 2 and 3)
Link to Videos

The objective of this session is to cover foundational concepts needed to help you start working with Microsoft Azure. We will cover basic concepts and lay down the basics in preparation for the Azure Machine Learning and Data sessions. Topics include: introduction to cloud computing; Azure key components and hierarchy; setting up Azure subscription; and a walkthrough of cloud basics to get you started.

*Participants already familiar with Azure may register for Sessions 2 and 3 without previously completing Session 1. However we strongly recommend that you review the Session 1 syllabus to ensure you are comfortable with the material. Sessions 2 and 3 will not review Azure 101 topics.

Session 2.  Azure Data Management and Storage

This portion of the workshop is focused on introducing you to the Azure data platform, which includes the services and the tools available to enhance your work with your data. We will explore:

  • Basic unstructured data with an Azure Storage Account and NoSQL with Cosmos DB
  • Relational databases with Azure DB for MySQL
  • Combinations of structured and unstructured data with Azure Data Lake and Azure Data Factory

By the end of this session you'll be informed on the different technologies available for data storage in Azure, and best practices on when they should be used.

Session 3.  Azure Machine Learning

In the first half of the workshop, you will learn the most important concepts of the machine learning workflow that data scientists follow to build an end-to-end data science solutions on Azure. You will learn how to find, import, and prepare data; select a machine learning algorithm; train and test the model; deploy a complete model to an API. You will get tips, best practices, and resources you and your team need to continue your machine learning journey, build your first model, and more.

We will cover the following basic topics:

  1. What machine learning and when is machine learning the right tool
  2. How to select the right machine learning algorithm for your data science scenario on Azure
  3. How Azure Machine Learning tools will make your life easier
  4. Build a machine learning model with Azure Machine Learning designer
  5. Test, deploy and consume a machine learning model with Azure Machine Learning designer

In the second half of part 2 on advanced machine learning, we will introduce some challenges of deploying a machine learning model and we will discuss the following points in order to enable you to tackle some of those challenges:

  1. How to select the right tools to succeed with model deployment
  2. How model interpretability toolkits can be used for model training and deployment
  3. How to use Automated Machine Learning to optimize your machine learning deployment flow
  4. How to build multiple robust machine learning pipeline using tools such as Jupyter Notebooks, Virtual Machines and Containers
  5. How to register your model and transform it into a webservice that can be easily consumed by other data scientists and developers


Participants can register for any and all sessions. Note that Sessions 2 (Data Management) and 3 (Machine Learning) build on Session 1 (Azure 101.) See Couse Overview above for more details.

In-person Office Hours are limited to the first 20 registrants, after which a waitlist will become available.

Azure 101: Getting Started (Syllabus) Monday 10/26 Monday 11/2; 12:00-1:00pm EST Register
Azure Data Management and Storage (Syllabus) Monday 11/2 Monday 11/9; 12:00-1:00pm EST Register
Azure Machine Learning (Syllabus) Monday 11/9 Monday 11/16; 12:00-1:00pm EST Register


    Alex Vazquez is a Principal Consultant with Blue Chip - a CORE BTS Company.  He has worked on several large-scale cloud migration efforts and has assisted numerous organizations plan out their management strategies, both for migration and initial planning, as well as help operationalize the cloud. Currently focused on assisting Universities with their Cloud journey, Alex focuses on bringing his enterprise experience to assist researchers tackle their most complex problems.
    Francesca Lazzeri is a Cloud Advocate based in Cambridge, MA.  Francesca supports Machine Learning and AI projects on Azure, using tools such as Azure Machine Learning, Python on Azure, Jupyter Notebooks and VS Code for Machine Learning. Francesca has past experience working as a data scientist. You can find more about her at

    Jasmine Greenaway is a Cloud Advocate based in New York City. Jasmine has past experience working as a software engineer. Jasmine is able to support the development side of projects, such as: choosing data storage options and migrating existing data to Azure, serverless options in Azure, and general development with Azure including available tooling or APIs.


    Azure Data Syllabus.pdf143 KB
    Azure 101 Syllabus.pdf150 KB
    Azure Machine Learning Syllabus.pdf132 KB