GOVERNANCE
All model, experiment, and deployment history — centralized
Centrally track and manage who created and deployed which model, when, and with which data.
Products
NUBISON ML Platform
Reliably deploying developed AI models to production and sustaining their performance.
Standardize development, deployment, monitoring, and retraining on a single platform,
closing the gap between development and operations.
For AI models, what happens after development
is what really matters — operation
Reliably deploying developed models to production and sustaining their performance requires dedicated operational capabilities.
01
Limits of Code-Based Operations
Challenge
- Differences between individual code and environments undermine reproducibility and traceability.
NUBISON Solution
- Unified management of every model, experiment, and deployment history
02
Model Decay Without Retraining
Challenge
- Even when performance degrades due to data change, there is no framework for detection or retraining.
NUBISON Solution
- Single-platform coverage from development through operations and retraining
03
Fragmented Production Environments
Challenge
- Integrations across data, equipment, and AI systems are one-off.
NUBISON Solution
- Standard API-based integration with Datalake, IoT, and Agents
AI's competitiveness is decided in operation, not training.
Five core capabilities
that solve AI model operations
Standardize AI model operations from the perspective of five core capabilities.
FULL LIFECYCLE
Seven stages from development to production — all in one place
Perform every one of the seven stages from model development to production deployment on a single platform.
OPERATIONS
Standard operating process: Verify · Approve · Deploy
Apply models safely to production through phased approval, testing, and deployment.
INTEGRATION
Connect Datalake · IoT · Agent through standard APIs
Data ingestion and model service delivery are connected via standard APIs, ending one-off integrations.
ACCESSIBILITY
Business users can retrain and deploy directly
With a GUI-based environment, non-developers can perform retraining and deployment tasks on their own.
From development productivity to model reliability,
tangible change across four core areas
Deploy Time
70%
Model deployment time reduced
Automate from registration through deployment — cut deployment time by up to 70%
Traceability
100%
Action traceability
Complete audit trail recording every model, experiment, and deployment
Retraining
No-Code
Retraining automation
GUI-based retraining workflow — no code required to retrain and deploy
Drift Response
Auto
Automated drift response
Automatically detect data drift and trigger retraining and redeployment
From model creation to operation —
on a single platform
Cover every stage of development, testing, deployment, and operation through a seven-step standard process. Each stage is organically connected to support consistent AI operations.
- 1
Initial Development
Jupyter-based AI model prototyping
- 2
Code Registration
Register AI code and model assets
- 3
Code Conversion
Automatic conversion into a production-ready inference service
- 4
Knowledge · Management
Model, container asset, and version management
- 5
Inference Testing
Deploy inference server and validate functionality
- 6
Model Approval
Integrate with production environment and validate performance
- 7
Production Deployment
Automatic deployment to production after validation
Four-stage standard operating procedure
for reliable production rollout
Stage 01
Verify
- Unit testing
- Performance criteria validation
- Data compatibility check
- Security scan
Stage 02
Approve
- Change reason logging
- Administrator approval workflow
- Risk assessment
- Governance checklist
Stage 03
Phased Deployment
- Canary deployment
- Blue-Green switching
- Gradual traffic shift
- Instant rollback option
Stage 04
Monitor
- Real-time performance metrics
- Data Drift detection
- Automated incident alerts
- Retraining triggers
Beyond ML tooling —
operations-centric MLOps
Manage every stage in one place, with No-Code retraining and a standardized operations framework.
| Category | Existing ML tools | NUBISON ML Platform |
|---|---|---|
| User convenience | Most features implemented in code | Repetitive retraining and operations supported through No-Code |
| Infrastructure management | Limited management of CPU · GPU · NPU resources | Allocate and manage CPU · GPU · NPU resources per function |
| Feature completeness | Focused on model development, inference, and deployment; some operations features missing | Registration, asset management, testing, inference, deployment, and infrastructure management — all integrated |
| Data integration | Limited data integration; changes require rework | Organic integration with Datalake, flexible response to source changes |
Accelerate AI Adoption
Shorten the time it takes to move from PoC to production.
Operational Stability
Sustain model performance through continuous retraining.
Organization-wide Adoption
Build an AI operations environment driven by business users.