A good machine learning practitioner needs to know how to choose an algorithm for a particular application and how to monitor and respond to feed back obtained from experiments in order to improve a machine learning system. We recommend the following practical design process:

• Determine your goals—what error metric to use, and your target value forthis error metric. These goals and error metrics should be driven by the problem that the application is intended to solve.
• Establish a working end-to-end pipeline as soon as possible, including the estimation of the appropriate performance metrics.
• Instrument the system well to determine bottlenecks in performance. Diagnose which components are performing worse than expected and whether poor performance is due to overfitting, underfitting, or a defect in the data or software.
• Repeatedly make incremental changes such as gathering new data, adjusting hyperparameters, or changing algorithms, based on specific findings from your instrumentation.