NDN Model Studio
Fine-tune any model from Hugging Face using Google's enterprise-grade Vertex AI — then deploy it instantly to Firebase or download it as a portable artifact. No DevOps. No PhD required.
Fine-tuning is broken for everyone who isn't Google.
80% of enterprise AI value comes from customized models — not generic ones. But the tooling to get there sits behind steep infrastructure bills, scattered YAML configs, CUDA driver hell, and vendor lock-in. Teams with million-dollar budgets ship custom models. Everyone else ships prompts and hopes for the best.
Cloud bills you can't predict
Running a fine-tune job on raw Vertex AI requires custom training pipelines, quota increases, and a DevOps team just to track spend. A single mistake costs thousands before you realize it.
No unified workflow
Today: download model from Hugging Face, manually convert, push to a GCS bucket, write a custom training script, pray the job completes, figure out serving. That's 4–6 days for an ML engineer.
Deployment is a second project
Getting a trained model into production means spinning up a prediction endpoint, writing inference wrappers, managing versions, and monitoring — all before users see a single response.
Open source isn't enterprise-ready
Tools like Axolotl or LLaMA-Factory are powerful — but they require local GPU rigs or raw cloud VMs with days of setup. There's no managed, enterprise-secure alternative.
The model proliferation moment is here.
Hugging Face now hosts over 900,000 models. Meta, Mistral, Google, and the open-source community ship breakthrough models weekly. The barrier to a custom AI is no longer what model to use — it's how do I train and ship it fast.
Google's Vertex AI is the most powerful managed ML platform on the planet — but it's designed for ML engineers, not product teams. NDN Model Studio puts Vertex AI behind an interface any team member can actually use.
Firebase's serverless deployment means your newly trained model can be live in production — auto-scaling, zero cold start management — in under two minutes from training completion. No Kubernetes. No SRE ticket.
From dataset to deployed model in four steps.
No YAML files. No CUDA environments. No infra expertise required — just your data and a goal.
Pick any model from Hugging Face
Search 900,000+ open-source models — Llama 3, Mistral, Phi-3, Gemma, Qwen, Falcon, and more. Paste the model ID or browse by task. NDN Model Studio handles the download and conversion automatically.
Upload your training dataset
Drag-and-drop JSONL, CSV, or connect a BigQuery table. Configure your fine-tuning parameters — epochs, learning rate, LoRA rank — with intelligent defaults that work for most use cases.
Train on Google Vertex AI
We provision a managed Vertex AI training job — you watch a live progress dashboard. Enterprise-grade GPU clusters, automatic checkpointing, VRAM-aware batch sizing, and spend guardrails built in.
Deploy to Firebase or export
One click to deploy as a serverless Firebase Function endpoint — auto-scaling, zero cold-start config. Or export as GGUF, ONNX, or SafeTensors for your own infrastructure. You own the weights.
Built for builders, not just ML engineers.
Enterprise Teams
Customise a foundation model on your proprietary data — contracts, support tickets, internal docs — with data never leaving Google Cloud.
Researchers
Run reproducible fine-tuning experiments on Vertex AI at scale without managing clusters. Export results in standard formats.
Startups
Ship a differentiated AI product — a custom model trained on your vertical data — without hiring an ML platform team.
Developers
API-first access. Train via UI or programmatically. Webhook on completion. Integrate into your CI/CD pipeline. Your model, your infrastructure.
Be the first to train with NDN Model Studio.
We're onboarding a limited cohort of early users. You'll get direct access to the team, priority support, and founding-member pricing when we launch.