Distributed ML Engine
When the auditor asks "Why did the model reject this customer?" and operations needs "Reproduce exactly what ran Tuesday" — most ML platforms fall silent. LATTICE/MP doesn't.
Your data science team has trained hundreds of models. But when compliance asks questions, most platforms fail. LATTICE/MP was built from day one for the enterprise.
Same data + same params = same model. Every single time. Seeded PRNGs, stable MPI reductions, reproducible histogram binning. Zero surprises.
Every training run captures dataset hash, all parameters, per-node metrics, and SHA-256 verification. Court-admissible provenance.
Decision trees, rules, and linear models you can actually explain to regulators. SHAP values, feature importance, DOT export for visualization.
Control, compute, and data planes — cleanly separated for reliability and scale
Every algorithm fully distributed, deterministic, and production-tested at scale
Intel Xeon E5-2680v4, 10GbE interconnect. Near-linear scaling to 64+ ranks.
LATTICE/MP is for when you need to explain your model to a regulator, reproduce a training run from 6 months ago, or run where Python isn't an option.
| Feature | LATTICE/MP | Spark MLlib | scikit-learn | XGBoost |
|---|---|---|---|---|
| Deterministic Training | ✓ Yes | ~ Depends | ✓ Yes | ~ Depends |
| Full Audit Trail | ✓ Yes | ✗ No | ✗ No | ✗ No |
| No Python Runtime | ✓ Yes | ✗ No | ✗ No | ✗ No |
| Native MPI | ✓ Yes | ✗ No | ✗ No | ~ Partial |
| Explainable Models | ✓ Focus | ~ Mix | ~ Mix | ~ Limited |
| SHAP Values | ✓ Yes | ✗ No | ✗ No | ✓ Yes |
| Tenant Isolation | ✓ Yes | ~ Partial | ✗ No | ✗ No |
| Policy Enforcement | ✓ Yes | ✗ No | ✗ No | ✗ No |
When compliance, reproducibility, and explainability are non-negotiable
Credit scoring with explainable decisions. Model risk management with full reproducibility. Regulatory compliance built-in.
Clinical decision support with audit trails. FDA-grade reproducibility. Explainable diagnostics for physician review.
Air-gapped deployment. Zero external dependencies. Policy-controlled execution in secure environments.
Anomaly detection at the edge. Drift monitoring for predictive maintenance. Lightweight C++ runtime.
Underwriting models with feature importance. Claims fraud detection with explainable scores. Actuarial transparency.
Model governance that passes audits. Decision provenance for litigation. Hash-chained execution records.
Stop explaining why you can't reproduce last month's model. Start with LATTICE/MP.
