A machine learning problem solving checklist

2021-04-04 · Source: Brian Spiering’s Newsletter · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, quick

Summary

This checklist outlines a systematic approach to solving machine learning problems, particularly beneficial for those with less domain experience. It emphasizes reducing assumptions and avoiding common pitfalls by applying a structured methodology. Key steps include understanding data structure from first principles, explicitly defining system inputs and outputs, and mapping problems to established methods before developing novel solutions. The checklist also advises researching existing solutions, clearly defining evaluation metrics, empirically testing multiple models, and rapidly building an end-to-end system.

Key takeaway

For Data Scientists or Machine Learning Engineers tackling new problem domains, adopting a systematic checklist approach can significantly improve efficiency and reduce errors. Your focus should be on clearly defining problem parameters, leveraging existing solutions, and rapidly iterating on end-to-end system prototypes. This structured method helps ensure robust evaluation and faster deployment of effective solutions.

Key insights

A structured checklist approach enhances problem-solving in machine learning by reducing assumptions and avoiding common errors.

Principles

• Prioritize established methods over invention.
• Empirically validate model performance.
• Build end-to-end systems quickly.

Method

Understand data, define I/O, map to known methods, research solutions, define metrics, fit multiple models, and build an end-to-end system rapidly.

In practice

• Define inputs and outputs explicitly.
• Research existing solutions for similar problems.
• Create an end-to-end system quickly.

Topics

• Machine Learning Workflow
• Problem Definition
• Data Analysis
• Model Evaluation
• System Prototyping

Best for: Machine Learning Engineer, Data Scientist, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by Brian Spiering’s Newsletter.