MLOps is all about taking those cool ML experiments out of notebooks and into real-world apps without losing your mind over broken pipelines or stale data. In this deep-dive, you’ll see why you need containerization, CI/CD, robust monitoring and data-drift checks—using a banking fraud-detection scenario—and how it levels up traditional software practices for bulletproof model deployment.
On the toolbox side, expect to meet Docker, Kubernetes, MLflow, TensorFlow Data Validation, Prometheus, Grafana and more. You’ll walk through a full MLOps workflow (from data collection to production), learn why it’s become non-negotiable, and discover why data scientists, DevOps/cloud engineers and engineering managers should all get in on the action.
Watch on YouTube
Top comments (0)