---
title: "SageMaker | Skills We Assign For | Azendo"
description: "SageMaker as a managed ML platform — integration, cost, lock-in, plus the Azendo roles assigned for it."
url: "https://azendo.co/skills/sagemaker/"
---

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

# SageMaker.

SageMaker is AWS's managed machine learning platform, covering notebooks, training jobs, hyperparameter tuning, model registry, endpoints, pipelines and monitoring as integrated services.

## Where SageMaker fits on a long engagement.

Integration is the argument. Training on managed infrastructure, registering the result, deploying to an endpoint and monitoring it all work together without assembling a platform, which for a team without dedicated MLOps capacity removes months of work.

Cost needs active management. Notebook instances left running, endpoints provisioned for peak and never scaled down, and training jobs on oversized instances produce bills disproportionate to the work. Serverless inference and managed spot training address much of it and are underused.

## What an assigned team does with SageMaker.

Portability is the strategic cost. SageMaker-specific pipelines and endpoints are meaningful work to move elsewhere, and keeping training code framework-standard rather than SageMaker-idiomatic preserves that option cheaply.

Making that a deliberate choice rather than a default is architectural judgement assigned under [ai engineering services](https://azendo.co/services/ai-engineering/).

## What we use SageMaker for.

* A platform without building one Training through serving integrated, where dedicated MLOps capacity does not exist.
* Cost actively managed Idle notebooks and oversized endpoints found, which is usually the largest saving.
* Portability preserved deliberately Standard training code, so moving off the platform stays feasible.

## How SageMaker capacity is assigned.

Managed ML platform work is assigned inside AI capacity, with portability treated as a deliberate decision rather than an accident.

## Roles we assign SageMaker 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

* [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/)
* [Feast — skill we assign for](https://azendo.co/skills/feast/)
* [feature stores — skill we assign for](https://azendo.co/skills/feature-stores/)

## Tell us what your roadmap needs SageMaker 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/)