Categorizing Testing Strategies for ML Pipeline Readiness and Staleness
As an ML engineer, you have been tasked with ensuring the reliability of your machine learning pipeline. You understand that this requires you to monitor not only the readiness of the pipeline but also its potential staleness over time. The readiness of the pipeline is critical to ensure that individual components and the overall system are functioning as expected. On the other hand, staleness occurs when a model's performance decreases due to changes in data or the environment, causing inaccurate predictions. To tackle these challenges, you need to apply various testing strategies and keep a close eye on the pipeline's performance.
This exercise is part of the course
Developing Machine Learning Models for Production
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