ML FABRIC · AUTO-RESEARCH · MLOPS · ENTERPRISE INTELLIGENCE OS
78% of models never ship.
Yours will.
In 8 hours.
The world's most brilliant ML teams spend 60% of their time on infrastructure.
ML Fabric ends that AI does the engineering, your team does the science.
Architectures auto-searched
Faster to production
p99 serving latency
Registry to live endpoint
Registry to live endpoint
THE REAL PROBLEM · WITH EXISTING MLOPS PLATFORMS
They solve the easy bits. Not yours.
Seven stages. Every step instrumented, versioned, and governed. AutoResearch powers stages 1–3. The rest runs itself.
THE FULL ML LIFECYCLE · AUTO-ORCHESTRATED
Problem scoping, success metrics, data requirements — AI-assisted.
Click any node to inspect · Loop runs continuously
AUTO-RESEARCH · SIGNATURE CAPABILITY
AI finds the best architecture You ship it.
Every model, every drift signal, every serving metric unified in a single production view that tells you
what needs attention before it becomes an incident.
Winner: XGBoost + MLP ensemble — best F1 within 20ms latency budget. MLP captures non-linear interactions between session_duration × days_since_login that single-model architectures missed. TabNet had higher raw accuracy but failed latency constraint. Improvement vs manual baseline: +8.3%.
Your entire model fleet.
One dashboard. Real-time.
Every drift signal surfaces before it becomes an incident. Auto-retrain fires automatically. You just watch it fix itself.
DEPLOY SPEED · REGISTRY → LIVE ENDPOINT
8 hours. Not 8 days.
Registry to live REST endpoint. No DevOps ticket. No waiting. Watch it happen.
ALL CAPABILITIES
Everything your data estate needs.
One platform. Zero integrations.
Auto-cycling · Click any item to pin it
Auto-Research
AI Finds the Best Architecture
NAS + HPO + automated feature engineering. 247 architectures evaluated, 5,200 HPO trials. Winning model surfaced with full explainability, latency budget enforcement, and fairness checks.
DATASYNAIZE · ENTERPRISE INTELLIGENCE OS
ML Fabric trains on governed data.
Its outputs power autonomous action.
Data Fabric
Governed features → ML training Data quality → model quality Auto-retrain uses freshest data Zero ETL glue code
ML Fabric
Auto-Research · NAS + HPO Drift detection + auto-retrain One-click deploy · 14ms p99 No-code ML for analysts
Generative Fabric
ML scores → agent decisions Churn, fraud, demand → actions LLMOps + Agentic AI unified EU AI Act · GDPR · SOC 2
Every training run uses governed, quality-scored features. Auto-retrain pulls the freshest data automatically. No data team handoff.
Model predictions and embeddings ground LLM decisions. Churn probability, fraud score, demand forecast — all flow into autonomous action.
Conversational interface across all three fabrics. Ask about model drift, trigger a retrain, review experiment results without leaving chat.
READY TO SHIP?
Stop losing models to
production.