Evaluating and Enhancing Efficiency
In this chapter, we will explore the important aspects of rigorously evaluating the performance of active machine learning systems. We will cover various topics such as automation, testing, monitoring, and determining the stopping criteria. In this chapter we will use a paid cloud service, such as AWS, to demonstrate how an automatic, efficient active learning pipeline can be implemented in the real world.
By thoroughly understanding these concepts and techniques, we can ensure a comprehensive active ML process that yields accurate and reliable results. Through this exploration, we will gain insights into the effectiveness and efficiency of active ML systems, enabling us to make informed decisions and improvements.
By the end of this chapter, we will have covered the following:
- Creating efficient active ML pipelines
- Monitoring active ML pipelines
- Determining when to stop active ML runs
- Enhancing production model monitoring...
