Learn Live - Azure ML Fundamentals

Full series information: https://aka.ms/learnlive-202302FT More info here: https://aka.ms/learnlive-202302FT-Ep9 Follow on Microsoft Learn:

Session documentation: https://aka.ms/learnlive-20230404FT

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 1:05:55 - Deploy your Model to a Managed Endpoint, I Realtime Endpoint Demo 1:10:05 - Inferencing - Scoring against your model Endpoint 1:17:18 - 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 1:19:15 - 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 1:21:23 - Closure --------------------- Presenters Meer Alam Azure Customer Engineer Microsoft

LinkedIn: / meeralam

Marco Aurelio Cardoso Azure Customer Engineer Microsoft

LinkedIn: / marco-cardoso

Moderators Neeraj Jhaveri Senior FastTrack Engineer Microsoft

LinkedIn: / neerajjhaveri

Transcript

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Microsoft Developer

588K subscribers

Learn Live - Azure ML Fundamentals

Chapters View all

Welcome

Welcome

Welcome

Introduction

Introduction

Introduction

Learning Objectives

Learning Objectives

Learning Objectives

Where do we start? - Azure Machine Learning Service and Access Control

Where do we start? - Azure Machine Learning Service and Access Control

Where do we start? - Azure Machine Learning Service and Access Control

Azure Machine Learning Studio - Let us create our Compute for Data Science activities

Azure Machine Learning Studio - Let us create our Compute for Data Science activities

Azure Machine Learning Studio - Let us create our Compute for Data Science activities

Authoring Experience for your Notebook - Use Azure ML Python SDK to manage our ML Model Life Cycle

Authoring Experience for your Notebook - Use Azure ML Python SDK to manage our ML Model Life Cycle

Authoring Experience for your Notebook - Use Azure ML Python SDK to manage our ML Model Life Cycle

Create Data Assets from your choice of Data Store to train your ML Model.

Create Data Assets from your choice of Data Store to train your ML Model.

Create Data Assets from your choice of Data Store to train your ML Model.

Model Authoring - Generate your model through Automated ML with high scale, efficiency, and productivity all while sustaining model quality - Demo

Model Authoring - Generate your model through Automated ML with high scale, efficiency, and productivity all while sustaining model quality - Demo

Model Authoring - Generate your model through Automated ML with high scale, efficiency, and productivity all while sustaining model quality - Demo

Microsoft Developer

Learn Live - Azure ML Fundamentals

Comments 2

Chapters

Welcome

Welcome

Welcome

Introduction

Introduction

Introduction

Learning Objectives

Learning Objectives

Learning Objectives

Where do we start? - Azure Machine Learning Service and Access Control

Where do we start? - Azure Machine Learning Service and Access Control

Where do we start? - Azure Machine Learning Service and Access Control

Azure Machine Learning Studio - Let us create our Compute for Data Science activities

Azure Machine Learning Studio - Let us create our Compute for Data Science activities

Azure Machine Learning Studio - Let us create our Compute for Data Science activities

Authoring Experience for your Notebook - Use Azure ML Python SDK to manage our ML Model Life Cycle

Authoring Experience for your Notebook - Use Azure ML Python SDK to manage our ML Model Life Cycle

Authoring Experience for your Notebook - Use Azure ML Python SDK to manage our ML Model Life Cycle

Create Data Assets from your choice of Data Store to train your ML Model.

Create Data Assets from your choice of Data Store to train your ML Model.

Create Data Assets from your choice of Data Store to train your ML Model.

Model Authoring - Generate your model through Automated ML with high scale, efficiency, and productivity all while sustaining model quality - Demo

Model Authoring - Generate your model through Automated ML with high scale, efficiency, and productivity all while sustaining model quality - Demo

Model Authoring - Generate your model through Automated ML with high scale, efficiency, and productivity all while sustaining model quality - Demo

Register your model to Azure ML Models registry

Register your model to Azure ML Models registry

Register your model to Azure ML Models registry

Deploy your Model to a Managed Endpoint, I Realtime Endpoint Demo

Deploy your Model to a Managed Endpoint, I Realtime Endpoint Demo

Deploy your Model to a Managed Endpoint, I Realtime Endpoint Demo

Inferencing - Scoring against your model Endpoint

Inferencing - Scoring against your model Endpoint

Inferencing - Scoring against your model Endpoint

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

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

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

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

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

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

Closure

Closure

Closure

Description

Chapters View all

Welcome

Welcome

Welcome

Introduction

Introduction

Introduction

Chapters

Chapters

End-to-End MLOps in Azure | Azure Machine Learning