MLOps: Production ML at Scale

MLOps (Machine Learning Operations) enables reliable, scalable deployment of ML models. Teams with mature MLOps practices deploy models 60-80% faster, reduce downtime by 70-90%, and catch production issues 10-20x faster.

Core MLOps Pillars

1. Model Deployment

• Containerization: Docker containers for reproducible deployments
• Orchestration: Kubernetes for auto-scaling and load balancing
• Model Serving: TensorFlow Serving, TorchServe, NVIDIA Triton
• API Gateways: Rate limiting, authentication, versioning
• Multi-stage Deployment: Dev → Staging → Canary → Production

2. Model Monitoring

• Performance Metrics: Accuracy, latency, throughput, error rates
• Data Drift Detection: Identify distribution shifts in inputs
• Concept Drift: Detect when model predictions degrade
• Explainability: Track feature importance and prediction rationale
• Alerting: Automated alerts for anomalies and degradation

3. CI/CD for ML

• Automated Testing: Unit tests, integration tests, model tests
• Data Validation: Schema validation, quality checks
• Model Versioning: Track model lineage and artifacts
• Automated Retraining: Trigger retraining on performance degradation
• Rollback Capability: Quick rollback to previous model version

4. Experiment Tracking

• Hyperparameter Logging: Track all training configurations
• Metrics Tracking: Log accuracy, loss, custom metrics
• Artifact Management: Store models, datasets, code versions
• Reproducibility: Recreate any experiment exactly

Technology Stack

Orchestration & Serving:

• Kubernetes + Docker for containerization
• TensorFlow Serving, TorchServe, or Triton for model serving
• AWS SageMaker, Azure ML, or GCP Vertex AI for managed deployment

Monitoring & Observability:

• Prometheus + Grafana for metrics
• Evidently AI or WhyLabs for drift detection
• Arize AI or Fiddler for ML monitoring

Experiment Tracking:

• MLflow, Weights & Biases, or Neptune.ai
• DVC for data version control

CI/CD:

• GitHub Actions, GitLab CI, or Jenkins
• Great Expectations for data validation

Implementation Roadmap

Month 1: Foundation

• Set up experiment tracking (MLflow/W&B)
• Containerize models with Docker
• Deploy first model to staging

Month 2: Automation

• Build CI/CD pipeline for training
• Add automated testing
• Deploy to production with canary rollout

Month 3: Monitoring

• Implement performance monitoring
• Add drift detection
• Set up alerting and on-call rotation

Month 4: Optimization

• Model optimization (quantization, pruning)
• Automated retraining pipelines
• Advanced A/B testing

Best Practices

1. Start Simple: Deploy one model well before scaling
2. Monitor Everything: You can't improve what you don't measure
3. Version Everything: Code, data, models, configs
4. Automate Testing: Catch bugs before production
5. Plan for Rollback: Always have a fallback strategy
6. Document Decisions: Model cards, data sheets, architecture docs

Case Study: Fintech Company

• Challenge: 3-4 weeks to deploy new models, frequent downtime
• Solution: Complete MLOps platform with Kubernetes, MLflow, monitoring
• Results:
  • Deployment time: 3-4 weeks → 2-3 days (-85%)
  • Model downtime: 12 hours/month → 0.5 hours/month (-96%)
  • Issue detection: 2-3 days → 15 minutes (200x faster)
  • Cost savings: ₹40L/year (infrastructure optimization)

Pricing

• Basic Setup: ₹15-30L (single team, 5-10 models)
• Advanced Platform: ₹50L-1.5Cr (multiple teams, 50+ models)
• Managed Services: ₹30-80L/year (AWS SageMaker, Azure ML)