MLOps

Deploying, monitoring, and managing machine learning systems in production.

Model Deployment

Serving models via APIs, containerization, and inference optimization.

Experiment Tracking

Logging experiments, comparing runs, and reproducible ML workflows.

End-to-end automation, CI/CD for ML, and orchestrating training workflows.

Model Monitoring

Drift detection, performance tracking, and observability for ML systems.

Model Optimization

Quantization, pruning, distillation, and efficient inference.

Data Versioning

DVC, data lineage, dataset management, and reproducible data workflows.

Centralized feature management, online/offline serving, and feature pipelines.

Infrastructure & Scaling

GPU clusters, distributed training, cloud ML platforms, and cost optimization.

Automated testing, validation pipelines, and continuous delivery of models.