edloh1 / azureml-mlops-example-scenario

Example Scenarios: MLOps with Azure Machine Learning

Overview

MLOps is a set of repeatable, automated, and collaborative workflows with best practices that empower teams of ML professionals to quickly and easily get their machine learning models deployed into production.

This repository provides prescriptive guidance when building, deploying, and monitoring machine learning models with Azure Machine Learning in line with MLOps principles and practices.

These example scenarios provided an end-to-end approach for MLOps in Azure based on common inference scenarios. The example scenarios will focus on Azure Machine Learning and GitHub Actions.

Note: the Azure MLOps (v2) Solution Accelerator is intended to serve as the starting point for MLOps implementation in Azure.

Getting Started

This repository contains several example scenarios for productionising models using Azure Machine Learning. Two approaches are considered:

• Standalone: where services consuming models are operated entirely within Azure Machine Learning.
• Native integrations: where services consuming models within Azure Machine Learning are integrated within other services within Azure via out-of-the-box integrations.

Users of Azure Machine Learning might choose to integrate with other services available within Azure to better align with existing workflows, enable new inference scenarios, or gain greater flexibility.

All example scenarios will focus on classical machine learning problems. The IBM HR Analytics Employee Attrition & Performance dataset is available on Kaggle will be used to illustrate each example scenario.

Standalone deployments within Azure Machine Learning

Example Scenario	Inference Scenario	Description
Batch Managed Endpoint	Batch	Consume a registered model as a batch managed endpoint within Azure Machine Learning for high-throughput scenarios that can be executed within a single Azure Machine Learning workspace.
Online Managed Endpoint	Online	Consume a registered model as an online managed endpoint within Azure Machine Learning for low-latency scenarios.

For detailed instructions to deploy example scenarios for batch managed endpoints and online managed endpoints to a personal Azure environment see the Step-by-Step Setup section of this repository. This will result in a machine learning model being trained, registered in both environments, and deployed as both types of endpoints.

Native integrations between Azure services and deployments within Azure Machine Learning

Example Scenario	Inference Scenario	Description
Azure Data Factory / Synapse Pipeline	Batch	Consume a registered model as a batch managed endpoint within Azure Machine Learning for high-throughput scenarios orchestrated via Azure Data Factory or Azure Synapse Pipelines.
Azure Synapse Dedicated SQL Pool	Batch	Consume a registered model within a SQL Stored Procedure for high-throughput scenarios when loading data into an Azure Synapse Dedicated SQL Pool.
Power BI	Online	Consume a registered model deployed as an online managed endpoint within a Power BI report
Azure Stream Analytics	Streaming	Consume a registered model deployed as an online managed endpoint within an Azure Stream Analytics User Defined Function for processing high-volume data streams.

License

Details on licensing for the project can be found in the LICENSE file.