Platform · Enterprise MLOps
Govern every production model.
Enterprise MLOps deploys, monitors, governs, and continuously improves the fab-specific AI models behind the platform - keeping every model accurate, auditable, and accountable throughout its operational lifecycle.
Why it matters
Where Enterprise MLOps earns its keep.
Production AI models degrade silently. Sensor profiles shift, equipment ages, and recipes change - and a model that was accurate at deployment quietly drifts out of spec long before anyone notices on the line.
Enterprise MLOps automates the full lifecycle - training, validation, deployment, monitoring, and retraining - so every model stays accurate as operations evolve, with complete lineage and governance built in.
Core capabilities
What Enterprise MLOps does.
The governed lifecycle behind every fab-specific model on the platform.
Centralized Model Registry
One governed repository for every production and historical model across manufacturing sites.
Automated CI/CD & Retraining
Ingest data, train, validate, and roll out new model versions as production conditions change - without disrupting live operations.
Closed-Loop Monitoring
Track prediction quality, data drift, and model accuracy continuously - alerting engineers before production or yield is affected.
Deterministic Lineage
Every dataset, feature set, hyperparameter, and deployment tracked for complete, reproducible auditability.
Automated Rollbacks
Instantly restore the previous production model whenever an anomaly or regression is detected.
Compliance & Governance
Audit-ready reports on model versions, validation metrics, deployment history, and data governance - generated automatically.
Reliable models, at fleet scale.
Move approved models into production in minutes instead of weeks.
Drift and degradation surfaced before silent failures affect yield.
Managed across fabs, lines, and equipment from a single platform.
Figures are representative of early deployments and finalize per customer engagement.
One shared core
Every retrain makes the next model better.
Enterprise MLOps runs on the same deployment architecture as the whole platform. Each retrain - the drift it caught, the data it learned from, the version it locked in - is written back into the shared knowledge graph, where every module draws on it. Model expertise that once lived with a few data scientists becomes an asset the whole organization owns.
See the whole platformIn the field
“Our fab-specific models used to drift silently between retrains. Now degradation surfaces - and the model corrects itself - before it ever reaches the wafer.”
The rest of the platform
The models MLOps governs.
Four causal modules, all running on the lifecycle Enterprise MLOps manages.
Causal Diagnostics
Understand why equipment failed.
Explore CDCausal Process Intelligence
Keep every process stable and optimized.
Explore CPICausal Defect Feedback
Connect defects to their operational origin.
Explore CDFCausal Resolution Orchestration
Turn causal intelligence into operational execution.
Explore CROSecurity
Your data, your control.
We safeguard your information with advanced security protocols and strict compliance standards. Deploy in your VPC or fully on-prem / air-gapped - your data never leaves your environment.
SOC 2 Type IIAudited security controls
ISO 27001Information security management
CCPAData privacy complianceFAQ
Questions from the fab floor.
How is Causal Intelligence different from correlation-based analytics?
It models cause and effect across your operational knowledge graph, so you get the true driver with the evidence behind it - not just a correlated signal that leaves engineers guessing.
How long does a pilot take?
A typical pilot runs 60-90 days on your own operational data, with measurable impact on RCA cycle time by the end of the engagement.
Where does our data live?
Deploy in your VPC or fully on-prem / air-gapped - data never leaves your environment. We hold SOC 2 Type II, ISO 27001, and CCPA.
Which systems does it connect to?
MES, SPC/FDC, historians, EDA & yield, PLM, CMMS, and ticketing - all feed the CIP Core evidence layer through standard connectors.
Do we need a data-science team to run it?
No. Enterprise MLOps governs fab-specific models for you, so engineers work inside the resolution workflow instead of notebooks.
Govern every production model.
Deploy, monitor, and continuously improve your manufacturing AI models on Enterprise MLOps. See how ThirdAI Automation keeps operational AI accurate, explainable, and accountable - on your own data, in your own environment.
Book a demo