ML Experiment Tracking
Without proper experiment tracking, your ML team forgets what they tried, can't reproduce results, and rediscovers the same dead ends. We deploy experiment tracking that logs every run, compares metrics across experiments, and makes any historical result reproducible.
Need this done for your project?
We implement, you ship. Async, documented, done in days.
Platform Selection & Deployment
MLflow for open-source self-hosted, Weights & Biases for managed with rich visualization, or Neptune.ai for teams that need collaboration features. We deploy the tracking server with proper storage backends, authentication, and network policies. The choice depends on your team size, budget, and whether you need on-prem data residency.
Logging Integration
We integrate tracking into your training code with minimal boilerplate — auto-logging captures framework metrics (loss curves, learning rate schedules) without code changes. Custom metrics, hyperparameters, and artifacts get logged via a thin wrapper library. Training scripts that already work don't need rewriting — we add tracking on top.
Comparison & Visualization
Parallel coordinate plots, metric comparison tables, and artifact diff views let your team compare hundreds of runs. We configure custom dashboards for your specific metrics — accuracy vs. latency tradeoffs, resource usage per configuration, and performance across data subsets. Finding the best model configuration goes from 'check the spreadsheet' to 'filter and sort'.
Reproducibility Guarantees
Every logged run captures: code version (git commit), data version (DVC or hash), environment (Docker image or conda spec), hyperparameters, and random seeds. One command recreates any historical experiment. This isn't just good practice — it's a requirement for regulated industries and a sanity check for debugging production model issues.
Why Anubiz Engineering
Ready to get started?
Skip the research. Tell us what you need, and we'll scope it, implement it, and hand it back — fully documented and production-ready.