Deploying ML models in production environments.
Tools: Docker, Kubernetes, TensorFlow Serving, MLflow.
Applications: Real-time predictions in finance, recommendation systems, automated workflows.
Sub Tracks:
Model Deployment & Serving
Continuous Integration & Continuous Deployment (CI/CD) for ML
Model Monitoring & Performance Tracking
Scalable & Secure ML Infrastructure