MLOps2: Data Pipeline Automation with GCP

This module covers the fundamental differences between model training pipelines and inference pipelines in GCP environments. Students learn how to design and implement pipelines that effectively handle both training and prediction scenarios, understanding the unique requirements and constraints of each.

This module explores the concept of data and model drift in ML systems, along with the challenges of feedback loops. Students learn to identify, monitor, and address various types of drift that can affect model performance over time using GCP tools.

This module focuses on implementing monitoring systems with triggers and alarms for ML pipelines in GCP. Students learn to set up automated alerts and response mechanisms for detecting and addressing issues in model performance and system stability.

This module examines techniques for ensuring and maintaining model stability in GCP production environments. Students learn methods to evaluate, monitor, and improve the reliability and consistency of deployed ML models.

This module covers the principles and practices of Continuous Integration and Continuous Deployment/Delivery in ML pipelines using GCP. Students learn to implement automated testing, deployment, and delivery processes for ML models.

This module addresses ethical considerations and responsible practices in AI development and deployment on GCP. Students learn about fairness, transparency, accountability, and risk management in ML systems, following the "Responsible Data Science" framework.