Every engagement — from a tabular pilot to a recurring LoRA fine-tune — moves along the same rail. The rail is not a diagram; it is the actual state machine of your run, and it streams to your console in real time.
Each stage emits artifacts you can open, download and audit — the run is complete only when its artifacts exist.
Your dataset arrives over an encrypted upload (CSV, JSONL or image archives). We checksum it, sniff the schema, count classes and flag PII automatically — before a human ever touches it.
Deterministic splits, dedup, tokenization or featurization, class-balance analysis. Every choice is recorded in a prep manifest, so the run is repeatable a year from now.
Your run executes on managed workers under per-job time and memory limits. Loss, learning rate, throughput and GPU utilization stream to your console step by step.
Held-out suites sized to the template: AUC and calibration for tabular, macro-F1 for text, win-rate vs base for fine-tunes. Regression gates compare against your baseline — a run that fails its gate does not ship.
Versioned export to ONNX or safetensors with a signed model card documenting data, spec, metrics and limits.
One-click hosted endpoint with keys and usage metering, or take the artifact and run it in your own stack. Your model either way.
The pipeline is a product decision as much as an engineering one. Three rules shape everything on this site.
Every screen in the console maps to a real table in the system of record. If you can see it, you can download it.
Runs end in eval reports, not slide decks. Baselines are stated up front and gates are enforced in the pipeline.
FMS engineers operate the pipeline and watch the same telemetry you do. When something drifts, we see it at the same step you would.
The console is the product. Sign in to watch a run stream, or start scoping yours.