Summary
In this chapter, we have delved deeply into the crucial aspects of rigorously evaluating the performance of active ML systems. We began by understanding the significance of automating processes to enhance efficiency and accuracy. The chapter then guided us through various testing methodologies, emphasizing their role in ensuring robust and reliable active ML pipelines.
A significant portion of our discussion focused on the criticality of the continuous monitoring of active ML pipelines. This monitoring is not just about observing the performance but also involves understanding and interpreting the results to make data-driven decisions.
One of the most pivotal topics we covered was determining the appropriate stopping criteria for active ML runs. We explored how setting pre-defined performance metrics, such as accuracy and precision, is crucial in guiding these decisions. We also emphasized the importance of a diverse and representative test set to ensure the model’...
