March 14, 2025
Building The Most Scalable Experiment Tracker For Foundation Models
At a large-scale model training (in huge models), anomalies are not rare events but problematic patterns that drive failure. Detecting anomalies early in the process saves days of work and training. ML model training observability is not just about tracking metrics. It requires proactive monitoring to catch issues early and ensure model success, given the high cost of training on large GPU clusters. If you are an enterprise or a team operating a model, focus on three key areas: fine-tune your prompts to get the most effective outputs (prompt engineering), ensure that your model behaves safely and predictably, and implement