Course Tracks
MLOps & MLSecOps
Model deployment, monitoring, version control, and securing the full ML lifecycle. Built by an engineer who runs ML systems in production.
Model Serving & Deployment
Deploy models at scale with real serving infrastructure. BentoML, Ray Serve, TorchServe, and Kubernetes-native patterns for production ML.
Experiment Tracking & Versioning
MLflow, DVC, and Weights & Biases used by real ML teams. Version data, code, and models together so experiments are reproducible.
Drift Detection & Monitoring
Statistical tests, data quality checks, and performance monitoring that catch degradation before it impacts users. Build monitoring into models from day one.
Feature Stores & Pipelines
Feast, Tecton, and custom feature stores. Build feature pipelines that work consistently across training and serving without silent inconsistencies.
ML Security & Adversarial Robustness
Data poisoning, model extraction, adversarial examples, and secure ML development practices. What MLSecOps looks like in practice.
LLMOps Patterns
Operationalizing large language models. Fine-tuning pipelines, evaluation frameworks, prompt versioning, and cost management at scale.
Courses in this track
Available Now
ML Model Deployment & Monitoring
Ship ML models to production and keep them healthy. Serving infrastructure, drift detection, data quality checks, observability, and rollback strategies.
Advanced
Self-Paced
Securing the ML Lifecycle
Data poisoning, model extraction, supply chain risks, and adversarial robustness—security practices for every stage of the machine learning lifecycle.
Advanced
Self-Paced
Operationalize Your ML Systems
Pick any course individually. No subscription required.