Full ML lifecycle: data preparation, training, fine-tuning, evaluation, and deployment with multi-tier GPU acceleration.
ML Lifecycle
Dataset management, labelling, augmentation, and quality validation.
Multi-GPU and multi-node training with automatic scaling.
LoRA, QLoRA, and full fine-tuning for LLMs and domain models.
Hyperparameter logging, metric visualization, and run comparison.
One-click deployment with auto-scaling inference endpoints.
Version management, lineage tracking, and model governance.
How the entire Nebula ecosystem works together
The AI engineering interface — web, desktop, and workbench
11 domain-specific GPU-powered cloud environments
Interactive demonstrations across all capabilities
From intent to verified engineering artifact.