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Nebius just launched Data Lab inside @nebiustf!
And I think this is the missing piece in most LLM improvement workflows.
Fine-tuning itself is not the hard part anymore.
The hard part is the loop:
→ find useful production logs
→ isolate failure cases
→ clean and reshape the data
→ create a training dataset
→ run post-training
→ deploy the improved model
→ repeat without rebuilding everything
That’s exactly what Data Lab is trying to fix.
It turns inference logs and existing datasets into reusable training data inside Token Factory.
So instead of treating model improvement like a one-time cleanup project, you can run it like a loop:
Logs → curated dataset → post-training → better model → redeploy → repeat.
I also tested this in my own workflow for:
→ Dataset preparation
→ Teacher-student distillation
→ Data Lab batch inference
→ LoRA fine-tuning
→ Serverless adapter deployment
→ Model comparison with a Gradio app
The interesting part is that the workflow stays connected.
You are not jumping between random scripts, notebooks, storage exports, training tools, and deployment infra.
Data Lab makes the model improvement cycle much faster for production apps.
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