After identifying performance degradation and drift, the logical next step involves updating the model. Manual retraining and deployment processes are often insufficient for maintaining reliable ML systems in production due to their slowness and potential for error. This chapter concentrates on automating the model update cycle to ensure timely and safe responses to changing conditions.

You will learn to:

• Design effective triggers (threshold-based or event-driven) to initiate the retraining pipeline automatically when specific conditions, such as a drift magnitude $d > \theta$ or performance metric $m < \phi$, are met.
• Compare and select appropriate data strategies for retraining, including using sliding windows, incremental updates, or full historical datasets.
• Build automated validation steps to assess the quality and safety of newly trained candidate models before they impact users.
• Distinguish between batch retraining approaches and continuous online learning systems, understanding the trade-offs.
• Implement safe deployment patterns, such as canary releases and shadow testing, to minimize the risk associated with introducing new model versions.
• Develop automated rollback mechanisms to quickly revert to a previous stable model version if the newly deployed one underperforms or causes issues.