ddyta.ai
/services/integration/services/integration/fine-tuned-custom-models
frontierSTATUS: PIPELINEPartner delivery

Fine-Tuned/Custom ML Models

Purpose-built models for your specific use case

When off-the-shelf models aren't enough, we build custom ones. Fine-tuned on your data for your specific problem — whether it's fraud detection, content classification, or domain-specific language understanding. Delivered with training pipelines so you can retrain as your data evolves.

PythonPyTorchAzure AIMLflow
// service_overview
Status
Pipeline
Tier
frontier
Delivery
Partner
Steps
4-phase engagement

[STATUS: PIPELINE] — This capability is in active development. Design-partner slots are open. Contact us to co-develop it against your workload.

/01Capabilities

What we deliver.

Every capability is engineered for production from day one — tested, documented, and ready to deploy.

  • Fine-tuning of foundation models on your data
  • Custom classification and detection models
  • Domain-specific language model training
  • Automated retraining pipelines
  • Model evaluation and benchmarking
/02Engagement Process

How the engagement runs.

P.01

Data Preparation

Collect, clean, and label training data for your use case.

P.02

Model Training

Train and fine-tune models with rigorous evaluation.

P.03

Deployment

Deploy models with inference APIs and monitoring.

P.04

Maintenance

Set up retraining pipelines and drift detection.

/03Deliverables
D.01

Trained custom model(s)

D.02

Inference API

D.03

Training and retraining pipeline

D.04

Model evaluation reports

Ready to put this into action?

Tell us about your project and constraints. We'll respond with a technical point of view.

Get in touch
next_service →
AI-Native Custom Software Builds

Full-stack builds with AI as a core feature