Ship models with intent.

FROM THE SUPPLIED BRIEF

Tools are only the surface. The craft is the layer between a trained model and a dependable product.

Three foundations

Use these as a compact map for completing the work.

01 / INFERENCE

Make prediction a contract.

FastAPI gives a model a clear boundary: validated input in, predictable output out.

POST /predict
{ "features": [0.42, 0.18, 0.91] }

200 OK
{ "label": "approved", "score": 0.87 }

02 / SERVING

Choose the right tempo.

Online predictions answer now. Batch predictions process a known set later. Latency, cost, and freshness make the decision.

03 / LIFECYCLE

Give every model a memory.

Version datasets, code, weights, and evaluation results so a good decision can be reproduced.

DECISION TOOL

What is your prediction tempo?

Select the requirement that matters most.

Choose a constraint to get a serving recommendation.

VERSION LEDGER

Record a model identity you can reproduce. It persists after refresh.

Fixed serving recommendations and locally recorded model identities

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Read the explanation

Online, Batch and Hybrid are three source-defined serving recommendations. Each button selects a fixed paragraph and marks the serving foundation as explored. Bars compare three choices with zero actual deployed prediction services at one hundred fifty pixels per count. The page explains latency and throughput framing, but it does not call a model, send a prediction request, install FastAPI, schedule a batch or measure speed or cost. The displayed request and response are examples, not an executed API. Enter a model name and version. Add trims both and accepts the pair only when neither is empty, producing one displayed ledger row. Bars compare two required fields with one row at one hundred fifty pixels per count. The source permits duplicates and saves just those strings; it does not store code, datasets, weights or evaluation artifacts. A row is a local identity note, not proof a model build can be reproduced or that those assets exist. Viewing the inference card, choosing a serving mode and adding a version can record all three explored foundation keys. Bars compare three keys with two stored collections, seen and entries, at one hundred pixels per item. Reload restores those collections, but it does not restore the chosen serving recommendation. Download combines the current recommendation with recorded versions; the source writes literal backslash-n separators, not actual Markdown line breaks. These are local notes and interaction tracking, not proof of learning, model operation or a backend request.