Elevator pitch Fine-tune and deploy custom LLMs with a no-code interface backed by production infrastructure.
Industry Developer Tools / ML Ops
Problem
- LLM fine-tuning requires deep ML expertise and expensive compute resources.
- Managing training pipelines, datasets, and model versioning is fragmented across tools.
Solution
- Unified dashboard for dataset upload, training configuration, and model deployment.
- Automated infrastructure scaling and one-click model serving with API endpoints.
Tools
dataset-validator: Ingests CSV/JSON, detects schema issues, returns quality score.training-orchestrator: Accepts model type and hyperparameters, returns job ID and status.model-registry: Stores versioned checkpoints, returns downloadable weights and metrics.inference-endpoint: Deploys trained model, returns live API URL and rate limits.
Widgets
/dashboard: Real-time training progress, loss curves, and model comparison charts./api-console: Interactive endpoint tester with request/response logging.
Conversation starters
- "How do I upload my dataset and start a training job?"
- "Can I compare multiple model versions and roll back to a previous one?"