This blog post provides an insightful overview of Gaussian Processes (GPs), emphasizing their strengths in making predictions from limited data and offering uncertainty quantification. It discusses key features such as kernel length scale and the implications for model complexity, while also addressing GPs' limitations regarding big data and high-dimensional spaces. Moreover, it briefly explores alternatives for uncertainty quantification using neural networks through bootstrapping methods.