How MLMind Works
Our process is simple: collect data, detect waste, intervene at the right time and share the savings with you.
Our Four‑Step Process
Connect & Baseline
Install our lightweight agent or integrate via API. We quietly observe your training and inference jobs for 7–14 days to build a baseline. There’s no impact on performance and no need to share sensitive model data.
Detect & Report
Our detectors comb through the baseline data to identify OOM loops, duplicate runs, jobs with no artifacts and other inefficiencies. We generate findings with confidence scores, cost impact and recommendations.
Guard & Optimise
Turn on Guard to automatically warn, stop or block wasteful runs according to your chosen thresholds. You can run in dry‑run mode first to see what would happen without interrupting workflows.
Save & Share
As waste is eliminated, your cloud bill drops. At the end of the period we calculate the realised savings relative to the baseline and invoice 10% as our fee – you keep the remaining 90%.
Under the Hood
MLMind is built as a set of microservices that run alongside your ML pipelines. The API ingests run metadata and GPU metrics, the worker analyses runs and applies detectors, and the UI delivers real‑time insights. All data stays within your environment. Secrets and credentials are stored in your own vault or AWS Secrets Manager, and communications are encrypted end‑to‑end.
Ready to Get Started?
Begin your optimisation journey today. Use our ROI Calculator to estimate your potential savings or contact us for a free baseline analysis.