ML-Dive - ML Codebase & Log Debugger

Machine learning engineers often face two time-consuming problems when joining or debugging an ML project: understanding an unfamiliar codebase and finding the cause of a failed training run. ML-Dive is an AI assistant built specifically for these workflows. It uses IBM Bob 2.0 as the analysis engine and presents the results through a Streamlit application designed for ML engineers. The first workflow is Codebase Onboarding. A user provides a computer-vision repository, such as a Detectron2-style project. Bob analyzes the repository structure, important files, configuration, entry points, data locations, and training/inference workflow, then produces an onboarding report that gives the engineer a practical map of the project instead of requiring them to manually explore hundreds of files. The second workflow is Training Log Triage. A user provides a training log and its run configuration. Bob analyzes the run and produces a human-readable diagnosis together with structured anomaly data. ML-Dive surfaces issues such as loss spikes, overfitting, NaN/Inf loss, and stalled learning rates. Each detected anomaly can be presented with its severity, iteration range, and numerical evidence. The target users are machine learning engineers, computer-vision researchers, ML students, and developers joining existing ML projects or debugging model-training runs. ML-Dive combines both workflows into one interface instead of treating repository understanding and training debugging as separate tools. Bob performs the reasoning and analysis, while ML-Dive converts that output into an ML-focused interface with readable reports, loss charts, anomaly bands, learning-rate visualization, zoomed-in problem areas, and diagnosis cards. A key part of the design is the separation between analysis and presentation. The Streamlit application does not perform the anomaly detection itself. It renders structured outputs produced by the analysis workflow, making the interface easy to test and allowing the same rendering pipeline to work with both verified reference data and IBM Bob output. The project was built in Python using Streamlit, pandas, NumPy, and Altair. The repository also includes validation tests, structured anomaly schemas, prompts, demo data, and IBM Bob integration. ML-Dive turns a process that can require hours of manual repository exploration and log inspection into a guided workflow: provide the project and training data, let Bob analyze it, and get an actionable view of what the ML engineer needs to understand next.