Introduction
The gap between a working Jupyter notebook and a production ML system is enormous. Most ML projects fail not because of bad models, but because of deployment challenges. This guide covers the essential practices for production ML.
The MLOps Lifecycle
Phase 1: Development
- Data exploration and feature engineering
- Model training and evaluation
- Experiment tracking
Phase 2: Validation
- Model testing and validation
- Performance benchmarking
- Bias and fairness assessment
Phase 3: Deployment
- Containerization and packaging
- Infrastructure provisioning
- API development
Phase 4: Operations
- Monitoring and alerting
- Model versioning
- Continuous improvement
Containerization Best Practices
Dockerfile Essentials
A production ML Dockerfile should:
- Use a minimal base image
- Pin all dependency versions
- Separate model artifacts from code
- Include health check endpoints
- Run as non-root user
Container Optimization
- Multi-stage builds to reduce image size
- Layer caching for faster builds
- Security scanning in CI/CD
API Design for ML Models
Request/Response Patterns
Design APIs that are:
- Versioned - Support multiple model versions
- Documented - Clear input/output schemas
- Validated - Reject malformed inputs early
- Timed out - Prevent runaway inference
Batch vs Real-Time
Consider both patterns:
- Real-time: Low latency, single predictions
- Batch: High throughput, bulk processing
- Hybrid: Queue system for async processing
Monitoring Essentials
System Metrics
- Request latency (p50, p95, p99)
- Throughput (requests per second)
- Error rates
- Resource utilization (CPU, memory, GPU)
Model Metrics
- Prediction distribution
- Feature drift
- Model accuracy (when labels available)
- Business KPIs
Alerting Strategy
Set alerts for:
- Latency exceeding SLA
- Error rate spikes
- Prediction distribution shifts
- Input feature anomalies
Model Versioning
Version Everything
- Model artifacts
- Training code
- Feature engineering code
- Configuration files
- Training data (or references)
Deployment Strategies
- Shadow mode: Run new model in parallel, compare outputs
- Canary: Route small percentage to new model
- Blue-green: Instant switch with rollback capability
- A/B testing: Statistical comparison of business metrics
Handling Model Drift
Types of Drift
- Data drift: Input distribution changes
- Concept drift: Relationship between inputs and outputs changes
- Feature drift: Individual feature distributions shift
Detection Methods
- Statistical tests (KS test, PSI)
- Monitoring prediction distributions
- Tracking business metrics
Response Strategies
- Automated retraining pipelines
- Alert-based manual review
- Fallback to simpler models
Infrastructure Considerations
Scaling Patterns
- Horizontal scaling for stateless serving
- GPU sharing for efficient utilization
- Auto-scaling based on load
Cost Optimization
- Spot instances for training
- Right-sizing serving infrastructure
- Caching for repeated predictions
High Availability
- Multi-region deployment
- Load balancing
- Graceful degradation
Security Best Practices
Model Security
- Encrypt model artifacts
- Access control for model registry
- Audit logging for predictions
Input Validation
- Schema validation
- Range checking
- Adversarial input detection
Output Protection
- Rate limiting
- Watermarking (for generative models)
- Audit trails
Case Study: Fraud Detection System
We deployed a fraud detection model for a fintech client:
Challenges:
- Sub-100ms latency requirement
- 10,000+ predictions per second
- 99.99% availability SLA
Solution:
- Containerized model with GPU inference
- Kubernetes deployment with auto-scaling
- Redis caching for feature lookup
- Real-time monitoring with automatic rollback
Results:
- 45ms average latency
- 99.995% uptime
- 3x improvement in fraud detection rate
Conclusion
Production ML is an engineering discipline, not just data science. Success requires treating model deployment with the same rigor as any critical software system. Start with solid fundamentals, monitor everything, and iterate continuously.
Need help deploying your ML models? Contact our team for a technical consultation.