MLOps: the Factory's layer for custom model lifecycle
Reproducible experiments, automated retraining, drift and accuracy monitoring for data science teams.
Inherits the AI Factory's guardrails, evidence model and 24/7 operations. Your data scientists build the models.
What you get
Reproducible experiments
Every dataset, feature set and hyper-parameter version-controlled. Deterministic re-runs for audits or root-cause analysis.
Automated pipelines
Schedule- or event-driven retraining. Candidate models walk from staging to production with no manual steps.
Accuracy and drift monitoring
Live predictions compared to ground truth. Precision, recall and cost-per-prediction tracked with alerts on threshold breach.
Model registry and versioning
Every trained artefact tagged, reproducible and traceable back to dataset, code and hyperparameters.
Feature store
Shared feature definitions across teams with lineage tracking. Avoids duplicate pipelines and drift between training and serving.
Deployment strategies
Canary, shadow and A/B deployments for models. Gradual rollout with automated rollback on threshold breach.
One fixed monthly fee, everything included
Pipeline engineering
- Reproducible experiment workspaces
- Automated retraining and promotion pipelines
- Feature store and dataset versioning
- Accuracy and drift dashboard authoring
- Advisory on model architecture and tooling
Model operations
- GPU fleet sizing and rightsizing
- Training job orchestration
- Inference endpoint management
- Canary and A/B deployment strategies
- Incident response for training failures
Audit and provenance
- Deterministic re-runs for audit
- Model lineage and provenance records
- Training-data anonymisation records
- Fairness and bias monitoring
- Evidence trail into the Factory's evidence lake
How it works
MLOps runs week after week alongside your data science team.
Assess
Your models, data sources and lifecycle gaps. Onboarding and ongoing.
Build
Reproducible workspaces, retraining pipelines, drift and accuracy dashboards. Foundations your data scientists extend.
Operate
24/7 runtime oversight. Training and inference monitored. Failures caught and triaged before you see them.
Improve
Weekly iterations on pipelines, monitoring and tooling. Framework upgrades rolled forward.
Audited and certified
AWS Advanced Partner
ISO 27001 Certified
AWS SaaS Competency
See MLOps running against your models
Walk us through a current model and we will show you the lifecycle wrapped around it.
Frequently asked questions
What is managed MLOps on AWS?
Managed MLOps on AWS is the ongoing operation of the model lifecycle in your AWS accounts, from experiment tracking and pipelines to drift monitoring, deployment and evidence trails.
Does this replace our data scientists?
No. Your data scientists build the models. We run the lifecycle so they spend time on modelling, not plumbing.
Which frameworks and tools are supported?
TensorFlow, PyTorch, scikit-learn, XGBoost, LightGBM, MLflow, Kubeflow and SageMaker. Custom frameworks integrate through the same pipeline abstraction.
Where do models and training data live?
In your AWS account. Models in your registry. Training data in your S3.
What about GPUs and cost?
We size, monitor and rightsize the fleet. Spot and on-demand blended to hit your cost target.
What is the SLA?
99.9% monthly uptime on the lifecycle control plane. Inference endpoints sized to your latency target.
Can we start with MLOps without the rest of the AI Factory?
Yes. MLOps runs standalone for data-science teams or sits alongside Agentic Ops and Guardrails as part of the full Factory.