Train your own AI models for your specific requirements
Custom Model Training
No-code model training with transfer learning for industry-specific terminology and use cases
With Custom Training from deepsight cloud, you create your own AI models for your codebooks and categories – without programming skills. The no-code interface enables industry-specific classification in minutes.
How Custom Training Works
"Das Produkt hat eine tolle Qualität"
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Standard Models Don't Always Fit
Standard Models Don't Always Fit
Every industry has its own terminology. Standard NLP doesn't understand your specific terms. Examples: Medical ICD codes, legal terminology, automotive parts.
Industry-specific terminology is not recognized
Internal terms and codes are unknown
Domain expertise is missing for precise categorization
One-size-fits-all doesn't achieve the required accuracy
Die Lösung
No-Code Model Training
Train your own models without a data science team:
Transfer Learning
Use pre-trained models as a base for fast training
No-Code UI
Intuitive interface without programming knowledge required
Iterative Improvement
Continuous feedback-based training for higher accuracy
One-Click Deployment
Immediately ready to use in all workflows after training
Training Process
The Training Process
Your custom model in 5 simple steps
Prepare Data
Upload training data (CSV, JSON, Excel). At least 500 labeled examples.
Choose Model Type
Classification, NER, Sentiment, or Topic Modeling
Start Training
GPU-accelerated in cloud. Real-time ETA display.
Custom Training in Workflow
No-code interface for your own AI models
Erfolgsgeschichten
Success Stories
How companies use Custom Training
Medical: ICD-10 Codes
5000 medical letters → 94% accuracy in code assignment
- Automatic code suggestions save 20h/week
- Error rate reduced by 80%
- Compliance ensured
Legal: Contract Review
2000 contracts → 97% precision in risk clauses
- Legal department focuses on real risks
- Review time reduced from days to hours
- Liability risks minimized
Automotive: Feedback
Internal part names automatically categorized
- 18h manual work → 10min automatic
- Consistent categorization ensured
- Insights for product improvement
FAQ
Frequently Asked Questions
Minimum: 500 labeled examples. Optimal: 2000+. More data = better accuracy.
10 minutes to 2 hours, depending on data volume and model type.
In Enterprise plan: Yes. ONNX or PyTorch format for on-premise deployment.
Training credits in Enterprise plan. External use: €200-2000 per training.
Depends on data quality. Typical: 85-95% accuracy. Medical/Legal: up to 97%.
Yes! On-premise training for GDPR-critical data available in Enterprise plan.
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