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SolutionsMachine learning

Models that reach production, and stay there.

We turn notebooks into versioned training pipelines on Amazon SageMaker, with a reviewed release and monitoring that notices when the data moves.

Plan your ML platform

Definition

What is MLOps?

Building, releasing and running machine learning models the way good teams run software: versioned, tested, reviewed and monitored. On AWS, Amazon SageMaker Pipelines runs training, the SageMaker Model Registry holds approved versions, and SageMaker Model Monitor watches a model once it serves real traffic.

Credentials

AWS Services Partner
Advanced Tier
AWS service deliveries
8
AWS certifications
25+
AWS certified
Every engineer

What does production-ready mean?

A model is ready when you can rebuild it from code and data, and release it on purpose.

Train

Every model traces back to its code and data.

Training is a SageMaker Pipelines run, not a notebook session. A model that misses its quality bar is never registered.

aws sagemaker list-pipeline-execution-steps \    --pipeline-execution-arn $RUNPASSPreprocessData   SucceededPASSTrainModel       Succeeded   spot instancesPASSEvaluateModel    SucceededPASSCheckQuality     Succeeded   metric above thresholdPASSRegisterModel    Succeeded   v14 PendingManualApproval# code 3f9c2e1 · data s3://ml-data/churn/train/2026-10-16/
Release

Release is a reviewed decision.

Only an approved version in the Model Registry can reach the production endpoint, and every release is a change someone signed off.

deploy/churn-prod.jsonJSON+1−1
{  "EndpointName": "churn-prod",Removed:   "ModelPackage": "model-package/churn/13",Added:   "ModelPackage": "model-package/churn/14",v14  "InstanceType": "ml.m5.large",  "DataCapture": true}
Policy checkModel Registry approvalFAIL · PendingManualApproval→PASS · Approved

What happens when the data drifts?

SageMaker Model Monitor compares live traffic with the training baseline. A breach starts retraining, not a fire drill.

Model Monitor · basket_valueIllustrative
Illustrative chart: the distribution of basket_value in live traffic has shifted to higher values than the training baseline. The drift is above the monitor's threshold, so retraining starts. Training baselineLive trafficDrift above threshold · retrainLowerHigher basket_value

SageMaker Model MonitorData quality

Severity: medium

Feature drift on basket_value

Resourceendpoint/churn-prod

  1. 06:00Hourly check: distribution has moved from the baseline
  2. 06:01Alarm starts the retraining pipeline
  3. 07:40v15 trained, evaluated and registered
  4. Day 2Approved by the model owner and deployed

ResolvedRetrained on fresh data; the next check passed.

How do we get you there?

  1. Step 01

    Prove the use case.

    A baseline model, before any platform spend.

  2. Step 02

    Build the platform as code.

    SageMaker domain, VPC isolation, least-privilege IAM, in Terraform.

  3. Step 03

    Run it with your team.

    Pipelines and monitoring your team can operate.

FAQ

Common questions.

No. We start with one use case and a baseline model to prove the data supports it. The platform grows from what that first model needs, not the other way round.

Yes. We move existing notebooks into SageMaker Pipelines steps and keep your framework, whether that is scikit-learn, XGBoost or PyTorch.

Managed Spot Training for jobs that can resume from a checkpoint, endpoints sized to real traffic, and cost tags on every training job and endpoint, so each model's cost is visible.

Next step

Plan your ML platform with an engineer.

Bring one use case. We tell you what production takes.

Plan your ML platform