About this Event

Registration is required for one or both parts of the workshop.

This is a hands-on 2-hour applied workshop, where attendees will learn new concepts by building their solutions on Azure and interacting directly with the instructors. Register for one or both workshops.

The workshop will focus on the following two topics:

1. Data Management and Storage (first hour)
2. Azure Machine Learning (second hour)

Instructors:

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 https://www.linkedin.com/in/francescalazzeri/
  

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. 

PART 1: Data Management and Storage (12:00-1pm): 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 SQL
• Combinations of structured and unstructured data with Azure Data Lake and Azure Data Factor
• Real-time processing with Azure Stream Analytics.

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.

PART 2: Azure Machine Learning (1:00-200pm):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