MLOps: Production ML Done Right

MLOps applies DevOps principles to machine learning: automated pipelines, continuous monitoring, and reliable deployment. Organizations achieve 80% faster deployment, 10x better reliability, and 70% reduction in incidents.

Core Components

1. CI/CD for ML

Continuous Integration:

• Automated model training on code/data changes
• Unit tests for data, features, models
• Model performance benchmarks
• Version control for code, data, models

Continuous Deployment:

• Automated deployment to staging/production
• Canary releases and blue-green deployments
• Rollback mechanisms
• A/B testing infrastructure

2. Model Monitoring

• Performance Monitoring: Accuracy, latency, throughput
• Data Drift Detection: Input distribution changes
• Concept Drift: Target variable relationship changes
• Alerts: Automated alerting on degradation
• Dashboards: Real-time metrics visualization

3. Model Versioning & Registry

• Track model lineage (data, code, hyperparameters)
• Reproducible training
• Model comparison and selection
• Rollback to previous versions
• Tools: MLflow, W&B, DVC

4. Feature Store

• Centralized feature repository
• Consistent features across training and serving
• Feature reuse across models
• Point-in-time correctness
• Tools: Feast, Tecton, AWS SageMaker Feature Store

MLOps Architecture

Training Pipeline

1. Data Ingestion: ETL from sources (databases, APIs, files)
2. Data Validation: Schema validation, quality checks
3. Feature Engineering: Transform raw data to features
4. Model Training: Train multiple models, hyperparameter tuning
5. Model Validation: Evaluate on holdout set, compare to baseline
6. Model Registration: Save to model registry if passing criteria

Inference Pipeline

1. Feature Extraction: Transform input data (same as training)
2. Model Serving: Load model, make predictions
3. Post-processing: Format output, business logic
4. Logging: Log inputs, outputs, predictions for monitoring

Deployment Strategies

Batch Prediction

• Scheduled jobs (daily, hourly)
• Process large datasets
• Pre-compute predictions
• Example: Daily customer churn scores

Real-time (Online) Serving

• REST API endpoints
• Low latency (10-100ms)
• Scalable infrastructure
• Example: Fraud detection during transactions

Streaming

• Process events in real-time streams
• Kafka, Kinesis integration
• Example: Real-time recommendation updates

Best Practices

1. Automate Everything

• Automated training, testing, deployment
• Reduce manual errors
• Faster iteration cycles

2. Monitor Extensively

• Track model performance 24/7
• Detect drift early
• Set up alerting thresholds

3. Version Everything

• Code, data, models, configs
• Reproducibility is critical
• Enable easy rollbacks

4. Test Thoroughly

• Unit tests for features and models
• Integration tests for pipelines
• Shadow deployment before full rollout
• A/B test new models

5. Manage Technical Debt

• Refactor pipelines regularly
• Document everything
• Remove unused features and models

Tools & Platforms

End-to-End Platforms

• AWS SageMaker: Full MLOps suite
• Azure ML: Microsoft's ML platform
• Google Vertex AI: GCP ML platform
• Databricks: Unified data + ML platform

Open Source Tools

• MLflow: Experiment tracking, model registry
• Kubeflow: ML on Kubernetes
• DVC: Data version control
• Feast: Feature store
• Airflow: Workflow orchestration

Case Study: Fintech Fraud Detection

• Challenge: Manual deployment, 2-week release cycles, frequent production issues
• Solution: Implemented MLOps with CI/CD, monitoring, feature store
• Results:
  • Deployment time: 2 weeks → 1 day (-93%)
  • Model refresh frequency: Monthly → Daily
  • Production incidents: 8/month → 0.5/month (-94%)
  • Model performance: +12% (faster iteration)
  • Team productivity: +200% (automation)