Step 8: Deploy ML Model to Production | Complete MLOps Guide

From Notebook to Production System

The architecture decisions that determine whether your model succeeds in the real world

Deploying an ML model to production is fundamentally different from running experiments. A production system must handle real traffic, be observable, fail gracefully, and update without downtime. Step 8 bridges the gap between a trained model artifact and a live, reliable, scalable service.

🧪Train & Validate

→

📦Serialize Model

→

🔌Build API

→

🐳Containerize

→

☁️Deploy Cloud

→

📊Monitor

Real-Time

🔌

Online Inference

Real-time predictions via REST or gRPC API. Millisecond latency required. Used in fraud detection, recommendations, and chatbots.

Batch

📂

Batch Inference

Scheduled bulk predictions on large datasets. Ideal for nightly reports, email recommendations, and data pipeline enrichment.

Streaming

⚡

Streaming Inference

Continuous predictions on event streams via Kafka or Kinesis. Used in IoT anomaly detection and real-time scoring pipelines.

Building a REST API — FastAPI Example

The standard pattern for exposing your trained model as a production service

The most common deployment pattern wraps your model in a web API. FastAPI is the modern choice — it provides async support, automatic OpenAPI docs, Pydantic validation, and excellent performance for ML workloads.

// app.py — Production FastAPI Inference Server

Python · FastAPI

# Step 8: Production Model API
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import joblib, numpy as np, time, logging
from prometheus_client import Counter, Histogram, generate_latest

# ── Prometheus Metrics ──────────────────────────
REQUEST_COUNT   = Counter('predict_requests_total', 'Total predictions')
REQUEST_LATENCY = Histogram('predict_duration_seconds', 'Latency')
ERROR_COUNT     = Counter('predict_errors_total', 'Total errors')

app = FastAPI(title="ML Model API", version="1.0.0")

# ── Load model ONCE at startup (critical!) ───────
@app.on_event("startup")
async def load_model():
    app.state.model  = joblib.load("model/classifier_v2.pkl")
    app.state.scaler = joblib.load("model/scaler.pkl")
    logging.info("✅ Model loaded")

# ── Schemas ─────────────────────────────────────
class PredictRequest(BaseModel):
    features: list[float]
    model_version: str = "v2"

class PredictResponse(BaseModel):
    prediction: int
    probability: float
    latency_ms: float

# ── Inference Endpoint ──────────────────────────
@app.post("/predict", response_model=PredictResponse)
async def predict(request: PredictRequest):
    start = time.time()
    REQUEST_COUNT.inc()
    try:
        X        = np.array(request.features).reshape(1, -1)
        X_scaled = app.state.scaler.transform(X)
        pred     = app.state.model.predict(X_scaled)[0]
        proba    = app.state.model.predict_proba(X_scaled)[0].max()
        latency  = (time.time() - start) * 1000
        REQUEST_LATENCY.observe(latency / 1000)
        return PredictResponse(
            prediction=int(pred),
            probability=round(float(proba), 4),
            latency_ms=round(latency, 2)
        )
    except Exception as e:
        ERROR_COUNT.inc()
        raise HTTPException(status_code=500, detail=str(e))

@app.get("/health")
async def health():
    return {"status": "healthy", "model": hasattr(app.state, "model")}

⚠️

Load your model once at startup, not per request. Loading a 500MB model on every inference call is a common mistake that will destroy latency and throughput.

Containerization with Docker

Package your model and environment into a portable, reproducible container

Docker ensures “it works on my machine” becomes “it works everywhere.” Your container captures exact Python versions, dependencies, and system libraries — eliminating environment drift between development, staging, and production.

// Dockerfile — Multi-Stage Production Build

Dockerfile

# Stage 1: Builder — install all dependencies
FROM python:3.11-slim AS builder
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir --user -r requirements.txt

# Stage 2: Minimal runtime image
FROM python:3.11-slim

# Security: run as non-root user
RUN addgroup --system mlapp && adduser --system --ingroup mlapp mlapp
WORKDIR /app
COPY --from=builder /root/.local /root/.local
COPY --chown=mlapp:mlapp . .
COPY model/ /app/model/

USER mlapp
EXPOSE 8000

ENV PYTHONUNBUFFERED=1 MODEL_VERSION=v2 LOG_LEVEL=INFO

HEALTHCHECK --interval=30s --timeout=10s --retries=3 \
  CMD curl -f http://localhost:8000/health || exit 1

CMD ["uvicorn", "app:app", \
     "--host", "0.0.0.0", "--port", "8000", "--workers", "4"]

// docker-compose.yml — Full Stack with Monitoring

YAML · Docker Compose

version: '3.9'
services:
  ml-api:
    build: .
    ports: ["8000:8000"]
    volumes: ["./model:/app/model:ro"]
    deploy:
      resources:
        limits: { memory: 2g, cpus: '2' }
    restart: unless-stopped

  prometheus:
    image: prom/prometheus
    volumes: ["./prometheus.yml:/etc/prometheus/prometheus.yml"]
    ports: ["9090:9090"]

  grafana:
    image: grafana/grafana
    ports: ["3000:3000"]
    environment: ["GF_SECURITY_ADMIN_PASSWORD=secret"]

Cloud Platform Deployment

AWS, GCP & Azure — each with managed ML serving solutions

AWS

☁️

AWS SageMaker

Managed inference endpoints with auto-scaling. Supports real-time, async, and batch transform. Built-in A/B testing via production variants.

GCP

🌐

Vertex AI

Google’s unified ML platform with online/batch predictions, drift monitoring, and AutoML. Strong TensorFlow ecosystem support.

Azure

🔷

Azure ML

MLflow-native deployment with blue/green endpoint routing. Excellent enterprise Active Directory and compliance integrations.

// AWS SageMaker Deployment — Python SDK

Python · SageMaker SDK

import boto3
from sagemaker import Model, Session
from sagemaker.model_monitor import DataCaptureConfig

session = Session()
role    = "arn:aws:iam::123456789:role/SageMakerRole"

# 1. Upload model artifact to S3
s3_uri = session.upload_data(
    path="model/model.tar.gz",
    bucket="my-ml-models",
    key_prefix="classifier/v2"
)

# 2. Define model
model = Model(
    image_uri="763104351884.dkr.ecr.us-east-1.amazonaws.com/sklearn:1.3",
    model_data=s3_uri, role=role, name="classifier-v2"
)

# 3. Deploy with data capture for monitoring
predictor = model.deploy(
    initial_instance_count=2,
    instance_type="ml.m5.xlarge",
    endpoint_name="classifier-production",
    data_capture_config=DataCaptureConfig(
        enable_capture=True,
        sampling_percentage=20,
        destination_s3_uri="s3://my-ml-models/capture"
    )
)

# 4. Auto-scaling: 2–10 instances
aas = boto3.client('application-autoscaling')
aas.register_scalable_target(
    ServiceNamespace='sagemaker',
    ResourceId='endpoint/classifier-production/variant/AllTraffic',
    ScalableDimension='sagemaker:variant:DesiredInstanceCount',
    MinCapacity=2, MaxCapacity=10
)
print(f"✅ Deployed: {predictor.endpoint_name}")

Deployment Strategies

How to ship new model versions without risking production traffic

Low Risk

🔵🟢

Blue / Green

Run two identical environments. Blue serves live traffic while Green hosts the new model. Switch instantly after validation — full rollback in seconds.

Gradual

🐤

Canary Release

Send 5% of traffic to the new model. Monitor metrics. Increase to 25%, 50%, 100% if healthy. Limits blast radius for failing models.

Experiment

🔬

A/B Testing

Route traffic by user segment to different model versions. Measure business KPIs and statistical significance before promoting.

Zero Downtime

🔄

Rolling Update

Replace pods one by one in Kubernetes. At each step: new pod up, old pod down. No complete shutdown, gradual capacity transfer.

// Kubernetes Canary Deployment

YAML · Kubernetes

# stable (v1) — 90% traffic via 9 replicas
apiVersion: apps/v1
kind: Deployment
metadata: { name: ml-api-stable }
spec:
  replicas: 9
  selector:
    matchLabels: { app: ml-api, track: stable }
  template:
    metadata:
      labels: { app: ml-api, track: stable }
    spec:
      containers:
        - name: ml-api
          image: myrepo/ml-api:v1.2.0
          resources:
            requests: { memory: "512Mi", cpu: "250m" }
            limits:   { memory: "1Gi",   cpu: "500m" }
---
# canary (v2) — 10% traffic via 1 replica
apiVersion: apps/v1
kind: Deployment
metadata: { name: ml-api-canary }
spec:
  replicas: 1
  selector:
    matchLabels: { app: ml-api, track: canary }
  template:
    metadata:
      labels: { app: ml-api, track: canary }
    spec:
      containers:
        - name: ml-api
          image: myrepo/ml-api:v1.3.0-rc1

Monitoring & Observability

Your model will degrade — know before your users do

⚠️

Model Drift Is Inevitable. Real-world data distributions shift. A model with 94% accuracy at launch may fall to 78% six months later without a single code change. Continuous monitoring is non-negotiable.

Category	What to Track	Tool	Alert Threshold	Action
Latency	p50, p95, p99 inference time	Prometheus + Grafana	p99 > 500ms	Monitor
Throughput	Requests/sec, errors/sec	Prometheus	Error rate > 1%	Monitor
Data Drift	Feature distribution shifts	Evidently AI, Whylogs	KL divergence > 0.1	Alert
Model Drift	Prediction distribution change	Evidently AI	PSI > 0.2	Alert
Concept Drift	Accuracy, F1 vs ground truth	Custom + MLflow	Accuracy drop > 5%	Retrain
Infrastructure	CPU, memory, GPU utilization	CloudWatch / Datadog	CPU > 80%	Scale

CI/CD Pipeline for ML

Automate everything from model validation to production promotion

// GitHub Actions — ML CI/CD Workflow

YAML · GitHub Actions

name: ML Model CI/CD
on:
  push:
    branches: [main]
    paths: ['model/**', 'app/**']

jobs:
  validate-model:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Run quality gates
        run: |
          python validate.py \
            --min-accuracy 0.92 \
            --max-latency-ms 200 \
            --test-set data/test.csv

  build-and-push:
    needs: validate-model
    steps:
      - name: Build & push Docker image
        run: |
          docker build -t myrepo/ml-api:${{ github.sha }} .
          docker push  myrepo/ml-api:${{ github.sha }}

  deploy-canary:
    needs: build-and-push
    environment: production
    steps:
      - name: Deploy canary (10% traffic)
        run: |
          kubectl set image deployment/ml-api-canary \
            ml-api=myrepo/ml-api:${{ github.sha }}
      - name: Monitor 5 minutes
        run: |
          sleep 300
          python check_canary_health.py \
            --error-threshold 0.01 --latency-threshold 300

  promote-stable:
    needs: deploy-canary
    if: success()
    steps:
      - name: Promote to stable (100%)
        run: |
          kubectl set image deployment/ml-api-stable \
            ml-api=myrepo/ml-api:${{ github.sha }}

ℹ️

Quality gates before every deployment. Enforce minimum accuracy, maximum latency, and schema compatibility. A pipeline that can ship bad models is worse than no pipeline at all.

Production Readiness Checklist

Tap each item to mark it complete — don’t ship until all are checked

Model artifact versioned and stored in a model registry (MLflow, W&B)
Inference API validates inputs — bad requests return 422, not 500
Health check endpoint responds in < 100ms and verifies model is loaded
Container runs as non-root user; no secrets baked into the image
Resource limits (CPU, memory) defined in Docker Compose / Kubernetes
Model performance validated on held-out test set before promotion
Latency benchmarked under expected load (locust or k6)
Logging structured as JSON — no bare print() statements in production
Prometheus metrics exposed: request count, latency histogram, error rate
Grafana dashboards configured with SLA-based alerting
Data drift monitoring active with alerts routed to on-call
Rollback procedure documented and tested — rollback achievable in < 5 min
Auto-scaling configured and tested under 2× expected peak load
Secrets managed via AWS Secrets Manager or Vault — not in .env files
CI/CD pipeline enforces quality gates before any production promotion

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