AutoML export artifacts
AutoML export artifacts are the files and package materials that help move a browser-local experiment into external review. In MLdeck, export language is intentionally verification-oriented: reports, manifest-driven package context, and ONNX-oriented export packages support testing and handoff, but they do not remove user-side validation responsibilities.
What export artifacts can include
Export artifacts can include a readable report, feature schema notes, manifest metadata, package files, sample input context, and ONNX-oriented model artifacts where supported by the workflow. The exact contents depend on the trained model and export path.
The important point is that an export is not just a score. Useful artifacts help reviewers understand the target, feature order, preprocessing assumptions, warnings, validation context, and what should be tested in another environment.
Manifest-driven package concept
A manifest-driven package uses public-safe metadata to describe what was produced. It can identify package contents, task type, feature schema, export status, and verification context without exposing private implementation details. This makes the package easier to inspect and less dependent on screenshots or memory.
Manifest context is especially important for CSV workflows because the model contract includes feature order, missing-value handling, categorical assumptions, and target configuration. Reviewers should read the package metadata before testing predictions.
ONNX-oriented packages need external testing
ONNX is useful because it is designed for portable model representation in supported runtimes. Portability still needs testing. Runtime versions, preprocessing mismatches, unsupported operators, numeric precision, and schema mistakes can all change behavior.
MLdeck positions ONNX-oriented export packages as artifacts for external validation and testing. They should be checked with representative rows, missing values, rare categories, boundary values, and the target runtime environment before any important use.
Reports, reproducibility metadata, and boundaries
Reports can document metrics, warnings, assumptions, and review notes. Reproducibility metadata can help reviewers understand the run context at a public-safe level, such as schema, task, artifact status, and package contents. These materials support review; they do not certify performance, compliance, or readiness for every external environment.
If source data is sensitive, treat generated reports, manifests, model files, and packages as sensitive derived artifacts. Share them only with the same care used for other analytics outputs.
Export artifacts FAQ
What are AutoML export artifacts?
AutoML export artifacts are files and package materials created after a training workflow, such as reports, schema notes, manifests, sample inputs, and ONNX-oriented packages where supported.
Is ONNX export the same as guaranteed deployment?
No. ONNX-oriented export packages are artifacts for external testing and verification. Users still need to test schema, preprocessing, runtime versions, edge cases, and target environment behavior.
What does manifest-driven package mean?
A manifest-driven package uses public-safe metadata to describe the model task, feature schema, package contents, and verification context so reviewers can inspect what was produced.
Should export artifacts be treated as sensitive?
Yes. If artifacts come from sensitive data, reports, schemas, and model files should be handled carefully as derived analytics assets.
Connect export with validation
Use these pages to move from browser-local training to careful artifact review.