Production ML Systems
Engineering practices for deploying, scaling, and maintaining AI systems in production environments.
ML != Software Engineering
ML systems have unique challenges: data dependencies, model drift, reproducibility, and probabilistic behavior. Production ML requires adapting traditional software engineering practices and adding ML-specific tooling.
MLOps Pipeline
Data Pipeline
- Collection: Ingestion from multiple sources
- Validation: Schema validation, data quality checks
- Processing: Feature engineering, transformation
- Versioning: Track dataset versions (DVC, lakeFS)
Training Pipeline
- Experiment tracking: MLflow, Weights & Biases
- Hyperparameter tuning: Automated search (Optuna, Ray Tune)
- Distributed training: Multi-GPU/multi-node (Horovod, DeepSpeed)
- Model registry: Version and store trained models
Deployment Pipeline
- Serving: REST API, gRPC, batch prediction
- A/B testing: Gradual rollout, shadow mode
- Model optimization: Quantization, pruning, distillation
- Infrastructure: Kubernetes, Docker, serverless
Monitoring & Observability
Model Performance
Track accuracy, latency, throughput
- Prediction distribution shifts
- Error rate by segment
Data Drift Detection
Monitor input distribution changes
- Statistical tests (KS, Chi-squared)
- Automated retraining triggers
System Health
Standard observability metrics
- CPU/GPU utilization
- Memory, latency
Business Metrics
Connect ML to business value
- Revenue impact
- User engagement
Serving Patterns
Online/Real-time Serving
Low-latency predictions (TensorFlow Serving, TorchServe, Triton)
Batch Predictions
Scheduled jobs for bulk inference (Spark, Beam)
Edge Deployment
On-device inference (TensorFlow Lite, ONNX Runtime, Core ML)
Key Challenges
- Reproducibility: Same code, different results due to randomness, hardware differences
- Technical debt: Hidden feedback loops, data dependencies
- Concept drift: Performance degrades as data distributions shift over time
- Scalability: Handling millions of predictions per second
- Cost optimization: GPU compute is expensive
Key Takeaways
- →ML systems require specialized infrastructure and tooling
- →Monitor model performance, data drift, and system health
- →Automate training, testing, and deployment pipelines
- →Production ML is about much more than just training models