📄️ Deployment
Strategies for serving machine learning models, including batch vs. real-time, containerization, and deployment patterns.
📄️ Monitoring
Detecting data drift, model decay, and system performance issues in production ML systems.
📄️ CI/CD for ML
Exploring Continuous Integration, Continuous Delivery, and Continuous Training in MLOps.
📄️ Data Versioning
Understanding how to track changes in datasets to ensure reproducibility and auditability in ML experiments.
📄️ Reproducibility
Ensuring consistent results across environments by versioning code, data, models, and environments.