---
title: "Azure ML | Skills We Assign For | Azendo"
description: "Azure ML in Microsoft estates — governance, integration, plus the Azendo roles assigned for it."
url: "https://azendo.co/skills/azure-ml/"
---

[Skills](https://azendo.co/skills/) MLOps and model delivery 

# Azure ML.

Azure Machine Learning is Microsoft's managed ML platform, covering compute, experiment tracking, model registry, endpoints and pipelines, integrated with Entra ID and the wider Azure estate.

## Where Azure ML fits on a long engagement.

For organisations standardised on Microsoft, the integration removes most of the friction. Identity through Entra ID, networking through existing virtual networks, and data in Azure storage mean machine learning arrives inside the existing governance rather than beside it.

Governance features are unusually strong, which matters in regulated sectors. Private endpoints, customer-managed keys, comprehensive audit logging and data residency controls are the things that make a compliance team approve a platform rather than block it.

## What an assigned team does with Azure ML.

Compute management is the recurring cost issue. Clusters configured without auto-scaling to zero, and compute instances left running after a session, are the standard findings on any Azure ML cost review.

Setting idle shutdown and scaling policy correctly is simple work with a direct saving, scoped alongside [devops managed services](https://azendo.co/services/cloud-and-devops/).

## What we use Azure ML for.

* ML inside existing governance Identity, networking and data controls already satisfying the compliance position.
* Regulated deployment Private endpoints and customer-managed keys where a compliance team must approve.
* Compute that scales to zero Idle shutdown configured, which is the standard cost finding.

## How Azure ML capacity is assigned.

Azure ML work is assigned inside AI capacity, with compute scaling policy set at setup rather than after a cost review.

## Roles we assign Azure ML for

* [MLOps Engineer MLOps engineering](https://azendo.co/services/mlops-engineering/mlops-engineer/)

## Service lines it sits in

* [MLOps engineering](https://azendo.co/services/mlops-engineering/)

Capacity is agreed as a committed monthly capacity across a discipline, not per skill.

## Related in mlops and model delivery

* [MLOps handoff — skill we assign for](https://azendo.co/skills/mlops-handoff/)
* [ONNX — skill we assign for](https://azendo.co/skills/onnx/)
* [TensorRT — skill we assign for](https://azendo.co/skills/tensorrt/)
* [Model registry — skill we assign for](https://azendo.co/skills/model-registry/)
* [Weights & Biases — skill we assign for](https://azendo.co/skills/weights-and-biases/)
* [Comet — skill we assign for](https://azendo.co/skills/comet/)
* [Neptune.ai — skill we assign for](https://azendo.co/skills/neptune-ai/)
* [DVC — skill we assign for](https://azendo.co/skills/dvc/)

## Tell us what your roadmap needs Azure ML for.

A service delivery manager replies with the disciplines we would assign, the monthly capacity and what the first month looks like.

[All skills we assign for](https://azendo.co/skills/)