The engineering problem

A team needs to fine-tune a 7B parameter language model for domain-specific document analysis. They need multi-GPU training, experiment tracking, model evaluation against a held-out test set, and deployment to an inference endpoint — with full lineage proving which training data, which hyperparameters, and which checkpoint produced the deployed model.

Or: a computer vision team needs to train an object detection model on 50,000 annotated images, compare three architecture variants, and deploy the winner with A/B testing. The winning model must be traceable back to its exact training run for regulatory compliance.

Why traditional approaches are insufficient

Infrastructure fragmentation. Training happens on one GPU cluster. Experiment tracking in a separate SaaS tool. Model registry in yet another. Deployment on a different platform entirely. No single lineage connects the deployed model to its training data.

Reproducibility failure. Six months after deployment, a model produces an unexpected output. Can you re-run the exact training job that produced it? With traditional tooling: probably not. Environment drift, dependency updates, and lost configuration mean the original training environment no longer exists.

Governance gap. Enterprise ML teams need to answer: which data was used to train this model? Who approved it for production? What are its known failure modes? Manual processes break under the weight of dozens of models across multiple teams.

How Studio executes ML workflows

Intent: "Fine-tune Qwen 2.5 7B for legal document classification. Training data: our annotated contract corpus (12,000 documents). Evaluation: F1 ≥ 0.92 on held-out test set. Output: deployed inference endpoint with A/B testing against baseline."

A representative Studio execution plan for this workflow:

1. Data preparation → validate corpus, split train/eval/test, compute statistics
2. Tokenization → configure tokenizer, validate sequence lengths
3. Training setup → LoRA configuration (r=16, alpha=32), learning rate schedule
4. Distributed training → multi-GPU (4x A100), DeepSpeed ZeRO-3
5. Evaluation → F1/precision/recall on test set, confusion matrix
6. Gate check → F1 ≥ 0.92? If no, adjust hyperparameters and retry
7. Model registration → version, tag, associate with training lineage
8. Deployment → inference endpoint with TensorRT optimization
9. A/B testing → route 10% traffic to new model, compare metrics
10. Verify → hash model weights, record full training lineage

The Execution Fabric orchestrates:

  • Multi-GPU provisioning (the AI/ML Workbench supports up to 8x H100 for large-scale training)
  • Mixed precision and memory optimization where supported by the training framework
  • Checkpoint management for long-running training (designed to resume from failure)
  • Scheduling optimization for non-urgent jobs
  • Scaling inference endpoints based on traffic patterns

The AI/ML Workbench provides:

  • GPU tiers: T4 (16GB) for prototyping, A10 (24GB) for medium training, A100 (40/80GB) for production training, H100 (80GB) for frontier models
  • LLM fine-tuning: LoRA, QLoRA, and full fine-tuning for Llama, Mistral, and Qwen architectures
  • Distributed training: DeepSpeed, FSDP, automatic mixed precision, multi-node support
  • Experiment tracking: Hyperparameter logging, run comparison, artifact versioning (W&B, MLflow integration)
  • Model deployment: One-click inference endpoints with TensorRT optimization, auto-scaling, and A/B testing. vLLM for LLM serving.
  • Model registry & governance: Version control, approval workflows, audit trail

Verified output: The deployed model carries complete lineage: which training data (checksums), which hyperparameters, which random seed, which checkpoint epoch, which evaluation metrics. The model weights are SHA-256 hashed. The training log is designed to be immutable once recorded. If the model needs to be audited, retrained, or rolled back, every decision point is traceable.

What distinguishes this from managed ML platforms

The difference is not the GPU infrastructure — many platforms offer that. The distinction is execution governance:

  1. Lineage is architectural, not opt-in. Every training run is designed to automatically capture full provenance — the architecture does not support untracked training.
  2. Deployment is part of the same execution. Training, evaluation, gating, and deployment are orchestrated as a single governed pipeline — not separate workflows stitched together with scripts.
  3. The same pipeline runs anywhere. A pharmaceutical company training models on patient data in a sovereign deployment uses the same Studio execution model as a cloud customer.

Deployment relevance

ML workflows handling sensitive data — healthcare records, financial transactions, classified intelligence — often cannot run in public cloud. The AI/ML Capability Pack is designed to deploy in sovereign and air-gapped environments using the same Studio execution model. Model training, evaluation, and serving run locally with zero external data movement.

This is Part 5 of the How Nebula Cloud Executes Work series.

Previous: Part 4 — Executing Geospatial Intelligence Next: Part 6 — Executing Scientific Computing · Part 7 — Building Immersive Experiences

Related: AI/ML Workbench · Capability Packs · Execution Fabric · Demo Catalogue

TopicsAi Machine Learning Model Training Llm Fine Tuning Distributed Training Execution Fabric