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serve Compress, deploy, and serve trained ML models in production. Covers model compression (quantization, pruning, distillation, ONNX export), inference APIs, containerization, CI/CD pipelines, monitoring, health endpoints, model versioning, and reproducibility packaging. Use when the user has a trained model and wants to reduce its size, deploy it, serve it, containerize it, build an inference API, set up monitoring, write a model card, create a CI/CD pipeline, or package for reproducibility.
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5 arquivos name serve description Compress, deploy, and serve trained ML models in production. Covers model compression (quantization, pruning, distillation, ONNX export), inference APIs, containerization, CI/CD pipelines, monitoring, health endpoints, model versioning, and reproducibility packaging. Use when the user has a trained model and wants to reduce its size, deploy it, serve it, containerize it, build an inference API, set up monitoring, write a model card, create a CI/CD pipeline, or package for reproducibility.
allowed-tools Bash(uv run * scripts/benchmark_model.py *) Bash, Read, Write, Edit, Glob, Grep
disable-model-invocation true argument-hint path to model file or project root (e.g. "model.joblib" or ".") model opus effort high compatibility >=1.0 metadata {"category":"mlops","tags":["deployment","serving","docker","fastapi","ci-cd","monitoring","model-card","onnx","quantization","compression"],"phase":"deploy"}
Model Serving & Deployment
Reference for taking trained models to production. Follow phases in order.
Phase 0: Compression (before deploying)
Reduce model size and latency before serving. Run benchmarks to confirm tradeoff is acceptable.
uv run ${CLAUDE_SKILL_DIR} /scripts/benchmark_model.py model.joblib model_compressed.joblib data/test.csv
Quantization
Dynamic (no calibration): torch.quantization.quantize_dynamic — easiest, CPUStatic (calibration required): torch.quantization.prepare + convert — faster inference4-bit (LLMs): bitsandbytes load_in_4bit=True — 4× memory reductionGPTQ : AutoGPTQ — accurate 4-bit for LLMs
Export
ONNX : skl2onnx for sklearn, torch.onnx.export for PyTorchONNX Runtime : InferenceSession — cross-platform optimized inferenceGGUF : convert_hf_to_gguf.py for local LLM inference
Pruning
torch.nn.utils.prune.l1_unstructured — remove smallest weightsStructured pruning: remove entire filters/heads for real speedup
Knowledge distillation
Student model learns from teacher's soft labels
Loss = α × CE(hard labels) + (1−α) × KD(soft labels, τ)
Benchmark targets
Metric Goal Latency p50/p95 < 2× original Throughput > 1.5× original Accuracy delta < 1% absolute Memory RSS < 50% original
See references/compression-guide.md
Phase 1: Pre-deployment checklist
Before writing any serving code:
python3 -c "import joblib, tracemalloc; tracemalloc.start(); joblib.load('model.joblib')")
requirements.txt pinned (pip freeze > requirements.txt)
Phase 2: Inference API patterns
Standard FastAPI template
from contextlib import asynccontextmanager
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel, Field
import joblib, numpy as np, logging, time
logger = logging.getLogger(__name__)
model = None
@asynccontextmanager
async def lifespan (app: FastAPI ):
global model
model = joblib.load("model.joblib" )
logger.info("Model loaded" )
yield
app = FastAPI(title="Model API" , version="1.0.0" , lifespan=lifespan)
class PredictRequest (BaseModel ):
features: list [float ] = Field(..., min_length=1 )
class PredictResponse (BaseModel ):
prediction: float
probability: float | None = None
latency_ms: float
@app.post("/predict" , response_model=PredictResponse
def predict (req: PredictRequest ):
start = time.perf_counter()
X = np.array(req.features).reshape(1 , -1 )
pred = float (model.predict(X)[0 ])
proba = float (model.predict_proba(X).max ()) if hasattr (model, 'predict_proba' ) else None
return PredictResponse(prediction=pred, probability=proba,
latency_ms=(time.perf_counter() - start) * 1000 )
@app.post("/predict/batch"
def predict_batch (requests: list [PredictRequest] ):
X = np.array([r.features for r in requests])
preds = model.predict(X).tolist()
return {"predictions" : preds, "count" : len (preds)}
@app.get("/health"
def health ():
return {"status" : "healthy" , "model_loaded" : model is not None }
@app.get("/ready"
def ready ():
if model is None :
raise HTTPException(503 , "Model not loaded" )
return {"status" : "ready" }
Async with background batching (high throughput) import asyncio
from collections import deque
request_queue: deque = deque()
async def batch_processor ():
while True :
if len (request_queue) >= 32 or (request_queue and await asyncio.sleep(0.005 ) is None ):
batch = [request_queue.popleft() for _ in range (min (32 , len (request_queue)))]
features = np.array([b['features' ] for b in batch])
preds = model.predict(features)
for item, pred in zip (batch, preds):
item['future' ].set_result(float (pred))
await asyncio.sleep(0.001 )
Model caching (multiple models) from functools import lru_cache
@lru_cache(maxsize=4 )
def load_model (version: str ):
return joblib.load(f"models/{version} /model.joblib" )
@app.post("/predict/{version}"
def predict_versioned (version: str , req: PredictRequest ):
m = load_model(version)
return {"prediction" : float (m.predict([req.features])[0 ])}
Phase 3: Containerization
Dockerfile FROM python:3.11-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY model.joblib .
COPY serve.py .
EXPOSE 8000
HEALTHCHECK --interval=30s --timeout=5s CMD curl -f http://localhost:8000/health || exit 1
CMD ["uvicorn", "serve:app", "--host", "0.0.0.0", "--port", "8000", "--workers", "2"]
docker-compose.yml services:
model-api:
build: .
ports:
- "8000:8000"
environment:
- MODEL_PATH=/app/model.joblib
volumes:
- ./models:/app/models:ro
restart: unless-stopped
healthcheck:
test: ["CMD" , "curl" , "-f" , "http://localhost:8000/health" ]
interval: 30s
retries: 3
Build and run docker build -t model-api:v1.0 .
docker run -p 8000:8000 model-api:v1.0
docker compose up -d
Phase 4: CI/CD (GitHub Actions)
name: Build & Deploy
on:
push:
branches: [main ]
paths: ['serve.py' , 'requirements.txt' , 'model.joblib' ]
jobs:
test:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: actions/setup-python@v5
with: { python-version: '3.11' }
- run: pip install -r requirements.txt pytest httpx
- run: pytest tests/ -v
build:
needs: test
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Build Docker image
run: |
docker build -t model-api:${{ github.sha }} .
docker tag model-api:${{ github.sha }} model-api:latest
- name: Smoke test
run: |
docker run -d -p 8000:8000 --name test-api model-api:latest
sleep 5
curl -f http://localhost:8000/health
docker stop test-api
Phase 5: Monitoring
Structured request logging import json, time
from fastapi import Request
@app.middleware("http"
async def log_requests (request: Request, call_next ):
start = time.perf_counter()
response = await call_next(request)
duration_ms = (time.perf_counter() - start) * 1000
logger.info(json.dumps({
"path" : request.url.path,
"method" : request.method,
"status" : response.status_code,
"duration_ms" : round (duration_ms, 2 ),
}))
return response
Prometheus metrics from prometheus_client import Counter, Histogram, generate_latest, CONTENT_TYPE_LATEST
from fastapi import Response
predict_counter = Counter('predictions_total' , 'Total predictions' , ['status' ])
predict_latency = Histogram('prediction_latency_seconds' , 'Prediction latency' ,
buckets=[.005 , .01 , .025 , .05 , .1 , .25 , .5 ])
@app.get("/metrics"
def metrics ():
return Response(generate_latest(), media_type=CONTENT_TYPE_LATEST)
Data drift detection (simple) import numpy as np
class DriftMonitor :
def __init__ (self, reference_stats: dict ):
self .ref = reference_stats
self .window = []
def record (self, features: list [float ] ):
self .window.append(features)
if len (self .window) >= 1000 :
self ._check_drift()
self .window = []
def _check_drift (self ):
arr = np.array(self .window)
for i, (feat, stats) in enumerate (self .ref.items()):
z = abs (arr[:, i].mean() - stats['mean' ]) / (stats['std' ] + 1e-8 )
if z > 3 :
logger.warning(f"Drift detected in {feat} : z-score={z:.2 f} " )
Phase 6: Model card template # Model Card: {model_name}
## Model details
- **Type** : {classification|regression}
- **Algorithm** : {e.g. XGBoost, RandomForest}
- **Version** : {semantic version}
- **Training date** : {date}
## Intended use
- **Primary use** : {task description}
- **Out-of-scope** : {what this model should NOT be used for}
## Performance
| Metric | Validation | Test |
|--------|-----------|------|
| {metric} | {val_ score} | {test_score} |
## Data
- **Training data** : {description, size, date range}
- **Features** : {N} input features
- **Target** : {description}
## Limitations
- {List known limitations}
## Ethical considerations
- {Bias considerations, fairness assessment}
Phase 7: Reproducibility package {project}/
├── model.joblib # trained model
├── config.json # hyperparameters + training config
├── data_manifest.json # dataset checksums + version
├── requirements.txt # pinned dependencies
├── serve.py # inference API
├── Dockerfile
├── README.md # how to reproduce training + how to serve
└── tests/
└── test_serve.py # API smoke tests
config.json {
"model" : "xgboost" ,
"experiment_id" : "exp004" ,
"hyperparameters" : { "max_depth" : 6 , "learning_rate" : 0.1 } ,
"training_date" : "2026-03-07" ,
"python_version" : "3.11" ,
"val_score" : 0.8634 ,
"test_score" : 0.8601 ,
"feature_count" : 42
}
Rules
Pin ALL dependencies in requirements.txt before containerizing
Always have /health and /ready endpoints — they are required for load balancers
Log every prediction with input hash, output, and latency
Never load models on every request — load once at startup
Test the Docker image locally before pushing to registry
Save config.json alongside every model artifact
