Picture this: it’s 2 AM. Your phone buzzes with a PagerDuty alert. The fraud detection model you deployed last week is suddenly letting through transactions it should be blocking. The precision has cratered — down 20%.
Before you can do ML on sequences, you need to understand what a sequence actually is in biology — and why you can't just feed raw letters into a classifier.
A one-page map of the MLOps lifecycle from training through deployment, monitoring, and retraining, pairing each stage to the production failure it catches.
Learn to build a retraining trigger that uses drift detection and performance monitoring to retrain ML models only when they need it, not on a fixed schedule.
Discover what a feature store is, how it eliminates training-serving skew by centralizing feature computation, and when your team actually needs one.
Models fail silently in production. Learn to detect data drift and prediction collapse with a lightweight Python dashboard before revenue drops.
Version your ML models with metadata, data hashes, and environment snapshots so you can roll back bad deploys in seconds — without guessing or retraining.
Learn how to detect data drift in production ML models using KS tests and Wasserstein distance, build a drift monitor, and respond when distributions shift.
Compare MLflow and Weights & Biases for ML experiment tracking, learn their tradeoffs on setup, cost, and collaboration, and pick the right tool.
Learn how to containerize your machine learning models with Docker and FastAPI to solve environment mismatches and deploy reliable production services.
Stop shipping models by hand — learn which ML pipeline steps to automate first, from pytest unit tests to GitHub Actions training and Docker deployment.
Learn why your ML model works in training but fails in production, and how to build a skew-resistant pipeline with shared features, validation, and monitoring.
Learn how to safely deploy ML models using shadow deployments and canary releases, with gradual traffic shifting, regression detection, and rollback strategies.