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Practical Design Process for Machine Learning Systems
A good machine learning practitioner must know how to select an algorithm for a specific application and how to monitor and respond to experimental feedback to improve the model. In the textbook Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville recommend a four-step practical design process:
- Determine Goals: Choose appropriate error metrics and set target values that are driven directly by the problem the application is intended to solve.
- Establish a Pipeline: Set up a working, end-to-end pipeline as soon as possible, including tools to estimate the selected performance metrics.
- Instrument the System: Monitor and analyze the pipeline to identify performance bottlenecks. Diagnose whether poor performance is due to overfitting, underfitting, or issues in the software or data.
- Iterate and Refine: Repeatedly make incremental adjustments—such as collecting more data, tuning hyperparameters, or selecting different algorithms—guided by the specific findings from your instrumentation.
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