How to Design Machine Learning Experiments — the Right Way

Successful machine learning projects depend more on proper experiment design than on simply adding more resources or using cutting-edge models. Asking the right questions is crucial, as throwing resources at a poorly defined problem typically results in inefficient solutions. This approach is demonstrated through several examples, including analyzing how grayscale images affect visual anomaly detection and testing the abstract reasoning capabilities of large language models. Further topics explored include uncovering causal relationships through experimentation and fine-tuning smaller vision models to outperform larger ones.