model-serving-and-inference

Model Serving and Inference

Purpose

Review or design how a trained model is served so that predictions are correct, fast enough, versioned, rollback-able, and monitored. Cover serving pattern selection (batch scoring vs online/real-time vs streaming), latency and throughput targets, request validation, model versioning and registry, safe rollout/rollback, training/serving skew, and production monitoring for input drift and prediction quality. The goal is a serving path where the deployed model version is known, can be reverted instantly, applies the exact same preprocessing as training, and raises an alert when inputs or outputs drift.

When to use

• A PR adds or changes a prediction endpoint, batch scoring job, or model-loading code.
• Inference latency or throughput does not meet a stated SLA.
• A model in production behaves differently from offline evaluation (skew suspected).
• There is no clear way to identify or roll back the currently-served model version.
• Drift, data quality at inference time, or prediction monitoring is missing.

When not to use

• The concern is how the model was trained (reproducibility/leakage) — use the ml-training-pipeline-review skill.
• The concern is the upstream feature ETL only — use the data-pipeline-review skill.
• The concern is the LLM prompt/eval rather than a deployed predictive model — use an LLM-specific review.

Procedure

1. Identify the serving pattern and entry point

find . -name "serve*.py" -o -name "predict*.py" -o -name "*inference*.py" -o -name "app.py" -o -name "main.py" 2>/dev/null | grep -v __pycache__ | head -30
grep -rn "@app.post\|FastAPI\|predict(\|load_model\|TorchServe\|BentoML\|mlflow.pyfunc\|triton" . --include="*.py" | head -25

Classify as batch (scheduled scoring to a table/file), online (sync request/response), or streaming.

2. Check model versioning and loading

grep -rn "model_version\|registry\|load_model\|stage=\|@latest\|MODEL_URI\|artifact" . --include="*.py" --include="*.yaml" | head -25

Confirm the served version is pinned and recorded (registry stage or explicit URI), not an implicit "latest" that changes silently.

3. Audit preprocessing parity (training/serving skew)

grep -rn "transform\|preprocess\|StandardScaler\|tokenizer\|feature_\|pipeline\|joblib.load\|pickle.load" . --include="*.py" | head -25

The serving path must apply the identical preprocessing artifact used at training (same fitted scaler/encoder), ideally a saved pipeline — not a hand-rewritten transform that can drift.

4. Check latency, throughput, and resource bounds

grep -rn "batch_size\|timeout\|max_workers\|torch.no_grad\|eval()\|@torch.inference_mode\|onnx\|half()\|to(device)" . --include="*.py" | head -25

Confirm inference uses no-grad/eval mode, batches where possible, sets request timeouts, and bounds concurrency.

5. Verify rollout, rollback, and monitoring

grep -rn "canary\|shadow\|rollback\|previous_version\|drift\|evidently\|prometheus\|log_prediction\|monitor" . --include="*.py" --include="*.yaml" | head -25

Confirm there is a documented rollback to the prior version and monitoring for input drift, prediction distribution, latency, and error rate.

Concrete checks

• Serving pattern (batch/online/streaming) matches the freshness and latency requirement.
• The served model version is explicit and recorded per prediction or per deploy (registry stage or URI).
• The exact training preprocessing artifact is reused at serving time (no hand-rewritten transform).
• Request inputs are validated (schema, types, ranges) before inference; bad input is rejected, not silently scored.
• Inference runs in eval/no_grad/inference_mode; batching is used where throughput matters.
• Online endpoints set a timeout and bound concurrency; no unbounded queueing.
• There is a one-step rollback to the previously-good model version.
• Rollout is staged (canary/shadow) for risky changes, not all-at-once.
• Predictions and inputs are logged (sampled) for monitoring and offline replay.
• Input drift and prediction-distribution drift are monitored with alert thresholds.
• Latency, throughput, and error rate are tracked against the SLA.
• Model artifacts are loaded from a trusted source; untrusted pickle is not deserialized.

Commands or Templates

Online endpoint with input validation, pinned version, and parity (FastAPI):

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel, conint, confloat
import joblib

app = FastAPI()
MODEL_VERSION = "churn:3"                     # pinned, not "latest"
pipeline = joblib.load("artifacts/churn_v3.joblib")  # same pipeline as training

class Features(BaseModel):                    # validates schema + ranges
    tenure: conint(ge=0, le=120)
    monthly_charge: confloat(ge=0, le=10_000)

@app.post("/predict")
def predict(f: Features):
    try:
        proba = pipeline.predict_proba([[f.tenure, f.monthly_charge]])[0][1]
    except Exception as e:
        raise HTTPException(status_code=400, detail=f"inference error: {e}")
    log_prediction(MODEL_VERSION, f.dict(), proba)   # for drift monitoring
    return {"model_version": MODEL_VERSION, "churn_proba": round(proba, 4)}

Efficient batch scoring (PyTorch):

import torch

@torch.inference_mode()                        # no autograd overhead
def score_batches(model, loader, device):
    model.eval()
    out = []
    for batch in loader:                       # batched, not row-by-row
        preds = model(batch.to(device)).cpu()
        out.append(preds)
    return torch.cat(out)

Input drift check with a population stability index (Python):

import numpy as np

def psi(expected, actual, bins=10):
    """Population Stability Index. >0.2 typically signals meaningful drift."""
    qs = np.quantile(expected, np.linspace(0, 1, bins + 1))
    e = np.histogram(expected, qs)[0] / len(expected) + 1e-6
    a = np.histogram(actual, qs)[0] / len(actual) + 1e-6
    return float(np.sum((a - e) * np.log(a / e)))

MLflow model registry promote/rollback (CLI):

# Promote a validated version to Production
mlflow models serve -m "models:/churn/Production" -p 5001 --no-conda
# Roll back: re-point the Production stage to the prior version
mlflow registry transition-stage --name churn --version 2 --stage Production

Common issues & anti-patterns

• Loading models:/name/latest so the served model changes silently on every new registration.
• Re-implementing preprocessing in the serving code, drifting from the training transform (skew).
• No input validation, so malformed or out-of-range requests get scored and produce garbage.
• Running inference with autograd enabled / in train mode, wasting latency and memory.
• Row-by-row scoring in a batch job where batching would be far faster.
• No timeout on an online endpoint, letting a slow model exhaust workers under load.
• No rollback path, so a bad model must be retrained instead of reverted.
• Deserializing a model from an untrusted pickle, enabling arbitrary code execution.
• No prediction logging, so drift and skew are invisible until business metrics drop.

Required output

Produce a structured report with:

1. Serving pattern — batch/online/streaming and fit to the freshness/latency requirement.
2. Versioning & rollback — how the served version is pinned/recorded and reverted.
3. Skew audit — preprocessing parity between training and serving, with file:line.
4. Performance — eval/no-grad/batching/timeout status vs SLA.
5. Monitoring — drift, prediction distribution, latency, and error-rate coverage.
6. Findings table — file:line | issue | risk | concrete fix.
7. Next safe action — single highest-priority remediation.

Safety

• Do not deploy, promote, or roll back a production model version during review without explicit approval.
• Never load or deserialize untrusted model pickles as part of the review.
• Treat inference inputs/outputs as potentially sensitive; redact PII and reference fields by name.
• Recommend shadow or canary rollout before sending live traffic to a new version.
• Do not disable monitoring or alerting to silence noise without sign-off.