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Scaling responsible MLOps with Azure Machine Learning | BRK21

589K subscribers

1.8K views 2 years ago

When MLOps and responsible AI (RAI) are fully operationalized, organizations can build trusted ML solutions, increase the rate of experimentation, and accelerate time to market. With new capabilities integrated into Azure Machine Learning (AzureML), you’ll be able to apply RAI throughout the ML lifecycle using the new RAI dashboard and scorecard to improve fairness, explainability, and model performance. You’ll also learn how to simplify ML workflows with AzureML’s latest productivity capabilities. Additional Resource: Microsoft Learn for Azure -- https://aka.ms/AzureMSLearn?wt.mc_id=... Latest News: Gain agility with an integrated data platform -- https://aka.ms/BuildMay22/Datablog Recommended Next Step: Get more detailed content on this topic with a curated Session Resource Page -- https://aka.ms/Build22/Resource/Azure... Microsoft Build 2022

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Scaling responsible MLOps with Azure Machine Learning | BRK21

40Likes

1,832Views

2022May 27

Azure Machine Learning

1:37

The Machine Learning Life Cycle

3:06

Training Models

3:48

Deploying the Model

4:34

Transcript

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Microsoft Developer

589K subscribers

Scaling responsible MLOps with Azure Machine Learning | BRK21

Key moments View all

Azure Machine Learning

Azure Machine Learning

Azure Machine Learning

The Machine Learning Life Cycle

The Machine Learning Life Cycle

The Machine Learning Life Cycle

Training Models

Training Models

Training Models

Deploying the Model

Deploying the Model

Deploying the Model

Azure Ml Studio Web User Interface

Azure Ml Studio Web User Interface

Azure Ml Studio Web User Interface

Create an Environment

Create an Environment

Create an Environment

Score Script

Score Script

Score Script

Create a New Endpoint

Create a New Endpoint

Create a New Endpoint

Microsoft Developer

Scaling responsible MLOps with Azure Machine Learning | BRK21

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Key moments

Azure Machine Learning

Azure Machine Learning

Azure Machine Learning

The Machine Learning Life Cycle

The Machine Learning Life Cycle

The Machine Learning Life Cycle

Training Models

Training Models

Training Models

Deploying the Model

Deploying the Model

Deploying the Model

Azure Ml Studio Web User Interface

Azure Ml Studio Web User Interface

Azure Ml Studio Web User Interface

Create an Environment

Create an Environment

Create an Environment

Score Script

Score Script

Score Script

Create a New Endpoint

Create a New Endpoint

Create a New Endpoint

Azure Ml Studio Ui

Azure Ml Studio Ui

Azure Ml Studio Ui

Components in Azure Ml

Components in Azure Ml

Components in Azure Ml

Create an Azure Ml Component

Create an Azure Ml Component

Create an Azure Ml Component

Azure Ml Environment

Azure Ml Environment

Azure Ml Environment

Github Action

Github Action

Github Action

Create a Data Set

Create a Data Set

Create a Data Set

Add Data Sets and Components to the Pipeline

Add Data Sets and Components to the Pipeline

Add Data Sets and Components to the Pipeline

Create an Azure Ml Endpoint

Create an Azure Ml Endpoint

Create an Azure Ml Endpoint

Create a Yaml File That Configures a Deployment

Create a Yaml File That Configures a Deployment

Key moments

Key moments

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