Know exactly what you are training on
Profile the full dataset, surface quality issues, preserve immutable versions, and keep content hashes attached.
Governed tabular AutoML / Self-hosted / Kubernetes-native
Build tabular models you can trust. Put them to work.
Profile the full dataset, compare up to 20 candidates, validate and explain your choice, then deploy on Kubernetes you control.
Your cloud. Your cluster. Your control.Keep sensitive model work inside your boundary.What is Sceptre?
Sceptre is a self-hosted, Kubernetes-native tabular AutoML and MLOps platform. It keeps profiling, training, validation, explainability, model promotion, deployment, and monitoring evidence inside one project-isolated workspace on infrastructure you control.
The model delivery gap
AutoML should not stop at the leaderboard.
A top score is only a candidate. Your team still has to prove it on new data, explain its decisions, preserve lineage, control compute, record promotion, and serve predictions safely.
Move from dataset to governed model API in one place.
Keep the data, experiment, model, and deployment record connected from the first profile to the final endpoint.
Choose a model you can defend
Compare task-specific metrics, diagnostics, parameters, and experiment history in one reviewable record.
Give reviewers the evidence
Validate on external data, inspect leakage controls, explain feature contributions, and download an audit document.
Deploy without starting a second project
Register, promote, deploy, monitor, stop, and fall back from the same project workspace.
Replace handoffs with one accountable workflow.
Without Sceptre
Datasets, notebooks, and model files spread across tools
With Sceptre
One project record for data versions, experiments, models, deployments, and evidence
From dataset to deployed model
Keep evidence connected from upload to endpoint.
Evaluate candidates, control resource use, prove the decision, and deploy without changing tools or losing context.
Read the quick start01
Bring the data
Create a project, upload an immutable dataset version, and uncover quality risks before training.
02
Set the guardrails
Confirm the task and target, choose up to 20 candidates, and review the resource estimate.
03
Build the evidence
Compare task metrics, validate on external data, and explain the strongest candidates.
04
Operate the winner
Register, promote, deploy, monitor, stop, and keep a fallback ready from the same workspace.
Give every reviewer the same evidence.
Help technical reviewers, risk teams, and decision makers assess the same model from one traceable record.
- Keep datasets, models, and predictions inside infrastructure you control.
- Package lineage, preprocessing, validation, and drift evidence for review.
- Promote, monitor, stop, or fall back through explicit actions.
Get platform discipline without building the platform.
Give a small ML or data team a repeatable operating process without assigning engineers to assemble every MLOps component.
Move from data to evidence in one workflow
Keep profiling, training, review, and deployment connected.
Make defensible model decisions
Put diagnostics, external validation, explanations, and lineage beside the model choice.
Protect shared Kubernetes capacity
Estimate demand before launch, cap each Job, and preserve room for higher-priority workloads.
Keep the operating record intact
Retain data versions, parameters, results, artifacts, and model status inside the project.
Frequently asked questions
Know exactly where Sceptre fits.
What is Sceptre?
Sceptre is a self-hosted, Kubernetes-native tabular AutoML and MLOps platform. It connects dataset profiling, model training, comparison, validation, explainability, promotion, deployment, and monitoring evidence in one project-isolated workspace.
Is Sceptre self-hosted?
Yes. You install Sceptre on a Kubernetes cluster using its public Helm OCI chart. The bundled PostgreSQL, SeaweedFS, and MLflow services run inside your environment; cluster operators can also configure approved external services and exposure.
How is Sceptre different from other AutoML platforms?
Sceptre is designed for the work around and after model comparison. It keeps external validation, leakage controls, SHAP explainability, staged promotion, authenticated deployment, drift analysis, and downloadable audit evidence connected to the project.
Does Sceptre support model explainability?
Yes. Sceptre provides on-demand and cached SHAP explanations, historical model reconstruction, and support for non-predictive clustering estimators.
What data formats does Sceptre support?
Sceptre supports CSV, Parquet, Excel, JSON, and JSONL.
What machine learning tasks can Sceptre handle?
Sceptre handles classification, regression, clustering, and time-series problems with task-specific ranking metrics.
Who is Sceptre for?
Sceptre is built for small ML and data teams that already run Kubernetes, work primarily with tabular data, and need self-hosted model evidence and operations without assembling a general-purpose MLOps platform.
Does Sceptre train deep-learning or distributed models?
Not in the current release. Sceptre focuses on in-memory tabular estimators. Multi-node or distributed training requires additional infrastructure and a backend such as Dask or Ray.
Is Sceptre ready for production use?
The current release is documented for local development, evaluation, and compatibility testing. Production and shared-cluster environments require the controls in Sceptre's production-readiness guide.
Evaluate Sceptre on your Kubernetes cluster.
Install the complete workspace, bring a tabular dataset, and follow one governed path from evidence to endpoint.