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AI/ML Workbench

Full ML lifecycle: data preparation, training, fine-tuning, evaluation, and deployment with multi-tier GPU acceleration from T4 through H100.

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Problem

ML infrastructure is the bottleneck — not the model.

Teams waste weeks configuring environments, fighting CUDA versions, and managing GPU scheduling. Training jobs fail silently. Model deployment requires entirely different tooling from training. And reproducing results from three months ago is nearly impossible.

ML Lifecycle

Train. Fine-tune. Deploy. Monitor.

Pre-configured PyTorch, TensorFlow, JAX, and HuggingFace environments with GPU auto-provisioning.

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Data Preparation

Dataset versioning with DVC, automated labelling pipelines, augmentation strategies, and data quality validation with lineage tracking.

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Distributed Training

Multi-GPU (up to 8x H100) and multi-node training with DeepSpeed, FSDP, and automatic mixed precision. Auto-scaling based on job requirements.

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LLM Fine-Tuning

LoRA, QLoRA, and full fine-tuning for Llama, Mistral, Qwen, and custom architectures. Quantization (GPTQ, AWQ) for deployment optimization.

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Experiment Tracking

Hyperparameter logging, training curves, metric comparison across runs, and artifact versioning. Built-in W&B and MLflow integration.

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Model Deployment

One-click deployment to inference endpoints with TensorRT optimization, auto-scaling, and A/B testing. vLLM for LLM serving.

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Model Registry & Governance

Version management, model cards, approval workflows, and lineage from training data through to production predictions.

GPU Tiers

Right-size your compute.

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T4 (16GB)

Inference, small model training, prototyping. Cost-effective for development cycles.

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A10 (24GB)

Medium model training, fine-tuning 7B-13B models, batch inference workloads.

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A100 (40/80GB)

Large model training, distributed workloads, 70B model fine-tuning with FSDP.

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H100 (80GB)

Frontier model training, maximum throughput. NVLink multi-GPU for 100B+ parameters.

Use Cases

What teams build here.

Enterprise AI Models

Custom classification, NER, and prediction models trained on proprietary data.

GeoAI Models

Satellite imagery segmentation, change detection, and spatial prediction.

Manufacturing AI

Defect detection, predictive maintenance, and quality optimization models.

Life Sciences

Drug discovery, genomics, and medical imaging model development.

Turn complex technical work into executable outcomes.

From intent to verified engineering artifact.

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Universal Workbench Capabilities

Available in the Nebula Cloud Universal AI Workbench

These capabilities ship inside the two Universal Workbench editions. Choose an edition and launch — no separate product to buy.

AI / ML Frameworks

GPU deep-learning frameworks and agentic AI tooling.

ApplicationAvailabilityLicence
CrewAI Ubuntu Built-in
Goose AI Ubuntu Built-in
Hermes Ubuntu Built-in
HuggingFace Transformers Ubuntu Built-in
JupyterLab Ubuntu 4.6 Built-in
LangChain Ubuntu Built-in
PyTorch Ubuntu 2.11 Built-in
TensorFlow Ubuntu 2.21 Built-in

Local LLMs

On-device large language models with zero data egress.

ApplicationAvailabilityLicence
DeepSeek Coder V2 16B Ubuntu Built-in
DeepSeek R1 8B Ubuntu Built-in
Llama 3.2 Ubuntu 3B Built-in
Nomic Embed Ubuntu Built-in
Ollama Windows Built-in
Qwen 2.5 Ubuntu 3B Built-in

Computer Vision

Detection, segmentation, and 3D perception.

ApplicationAvailabilityLicence
Kornia Ubuntu Built-in
ONNX Runtime GPU Ubuntu Built-in
Open3D Ubuntu Built-in
YOLO Ubuntu latest Built-in
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