Learn Live - Azure ML Operations

Full series information: https://aka.ms/learnlive-202302FT More info here: https://aka.ms/learnlive-202302FT-Ep4 Follow on Microsoft Learn:

Session documentation: https://aka.ms/learnlive-20230307FT

This live session will introduce the concepts of MLOps, discuss how organizations can adopt MLOps practices, and develop a machine learning lifecycle from a technical perspective. --------------------- Learning objectives

Operationalization of an end-to-end ML solution through Azure ML and DevOps practices

--------------------- Chapters -------- 00:00 - Welcome 02:08 - Introduction 02:47 - Learning Objectives 06:51 - Over of Machine Learning Operations (MLOps) 09:16 - Walkthrough the Mindmap of MLOps 16:04 - Data Preparation for your ML Model 20:19 - Azure ML Computes for your Model training 22:55 - Model Training Environment 24:45 - Azure Databricks has similar components like Data Assets, Compute, Environment like we have in Azure ML 28:31 - Authoring your models - Azure ML Studio Designer, Automated ML and Notbooks 32:00 - Azure ML Jobs 35:05 - Use Components to create Azure ML Pipeline 40:54 - Deploying your models to an Endpoint - Model Development Cycle Completed in Azure ML Studio 44:04 - How Does Registry help in MLOps? 47:05 - Leverage Azure ML Python SDK V2 for your Model Training 1:05:46 - Demo - ML pipelines with Python SDK V2 1:26:24 - DevOps for ML Solution - Walkthrough with Azure DevOps and GitHub Action to automate end-to-end operationalization of ML Solutions in Azure. 1:27:28 - Summary and conclusion --------------------- Presenters Andres Padilla Principal Customer Engineer Microsoft

LinkedIn: / andrespadillaandrade

Meer Alam Azure Customer Engineer Microsoft

LinkedIn: / meeralam

Moderators Kris Bock Machine Learning/Data Science, Azure FastTrack Engineering Microsoft

LinkedIn: / krisbock

Transcript

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Microsoft Developer

588K subscribers

Learn Live - Azure ML Operations

Chapters View all

Welcome

Welcome

Welcome

Introduction

Introduction

Introduction

Learning Objectives

Learning Objectives

Learning Objectives

Over of Machine Learning Operations (MLOps)

Over of Machine Learning Operations (MLOps)

Over of Machine Learning Operations (MLOps)

Walkthrough the Mindmap of MLOps

Walkthrough the Mindmap of MLOps

Walkthrough the Mindmap of MLOps

Data Preparation for your ML Model

Data Preparation for your ML Model

Data Preparation for your ML Model

Azure ML Computes for your Model training

Azure ML Computes for your Model training

Azure ML Computes for your Model training

Model Training Environment

Model Training Environment

Model Training Environment

Microsoft Developer

Learn Live - Azure ML Operations

Comments 2

Chapters

Welcome

Welcome

Welcome

Introduction

Introduction

Introduction

Learning Objectives

Learning Objectives

Learning Objectives

Over of Machine Learning Operations (MLOps)

Over of Machine Learning Operations (MLOps)

Over of Machine Learning Operations (MLOps)

Walkthrough the Mindmap of MLOps

Walkthrough the Mindmap of MLOps

Walkthrough the Mindmap of MLOps

Data Preparation for your ML Model

Data Preparation for your ML Model

Data Preparation for your ML Model

Azure ML Computes for your Model training

Azure ML Computes for your Model training

Azure ML Computes for your Model training

Model Training Environment

Model Training Environment

Model Training Environment

Azure Databricks has similar components like Data Assets, Compute, Environment like we have in Azure ML

Azure Databricks has similar components like Data Assets, Compute, Environment like we have in Azure ML

Azure Databricks has similar components like Data Assets, Compute, Environment like we have in Azure ML

Authoring your models - Azure ML Studio Designer, Automated ML and Notbooks

Authoring your models - Azure ML Studio Designer, Automated ML and Notbooks

Authoring your models - Azure ML Studio Designer, Automated ML and Notbooks

Azure ML Jobs

Azure ML Jobs

Azure ML Jobs

Use Components to create Azure ML Pipeline

Use Components to create Azure ML Pipeline

Use Components to create Azure ML Pipeline

Deploying your models to an Endpoint - Model Development Cycle Completed in Azure ML Studio

Deploying your models to an Endpoint - Model Development Cycle Completed in Azure ML Studio

Deploying your models to an Endpoint - Model Development Cycle Completed in Azure ML Studio

How Does Registry help in MLOps?

How Does Registry help in MLOps?

How Does Registry help in MLOps?

Leverage Azure ML Python SDK V2 for your Model Training

Leverage Azure ML Python SDK V2 for your Model Training

Leverage Azure ML Python SDK V2 for your Model Training

Demo - ML pipelines with Python SDK V2

Demo - ML pipelines with Python SDK V2

Demo - ML pipelines with Python SDK V2

DevOps for ML Solution - Walkthrough with Azure DevOps and GitHub Action to automate end-to-end operationalization of ML Solutions in Azure.

DevOps for ML Solution - Walkthrough with Azure DevOps and GitHub Action to automate end-to-end operationalization of ML Solutions in Azure.

Chapters

Chapters