Episode

FastTrack for Azure Season 2 Ep09: Azure ML Fundamentals

with Meer Alam, Marco Aurelio Cardoso, Neeraj Jhaveri

Learn Live

In the Azure ML Fundamentals session, you will get an understanding of the overall Azure Machine Learning (AzureML) components and how you can start using the AzureML studio web portal to accelerate you AI journey in the cloud.

Learning objectives

  • Intro to Azure ML Service
  • Implement ML solution in Azure ML Service and Azure ML Studio leveraging, Azure ML assets, notebooks, AutoML and SDK V2

Chapters

  • 00:00 - Welcome
  • 00:55 - Introduction
  • 02:02 - Learning Objectives
  • 13:58 - Where do we start? - Azure Machine Learning Service and Access Control
  • 23:05 - Azure Machine Learning Studio - Let us create our Compute for Data Science activities
  • 27:04 - Authoring Experience for your Notebook - Use Azure ML Python SDK to manage our ML Model Life Cycle
  • 34:27 - Create Data Assets from your choice of Data Store to train your ML Model.
  • 54:47 - Model Authoring - Generate your model through Automated ML with high scale, efficiency, and productivity all while sustaining model quality - Demo
  • 56:47 - Register your model to Azure ML Models registry
  • 01:05:55 - Deploy your Model to a Managed Endpoint, I Realtime Endpoint Demo
  • 01:10:05 - Inferencing - Scoring against your model Endpoint
  • 01:17:18 - Designer can help you put together a model pipeline very easily - creates the code for scoring script and creates the environment yml file for your model
  • 01:19:15 - Q & A - When you do not have a target variable for your model, un-supervised learning algorithm (regression) might the option you select during Automated ML
  • 01:21:23 - Closure

Connect

Intermediate
AI Engineer
Data Scientist
Data Analyst
Azure Machine Learning