Concepts

Datature Vi is a platform for building custom vision-language models (VLMs): models that read an image and text together (for example, answer an inspection question or place a box on a defect). You upload images, annotate them, fine-tune a model, and run inference. VLMOps is the name for that full lifecycle in one product, from labels to trained weights to how you ship predictions, similar to how teams talk about MLOps for traditional models.

If you are not technical, start with What Is Datature Vi? before this hub. The pages below explain how Datature Vi implements VLMs and related settings at mixed depth so you can stop when you have enough context.

New to VLMs?

Dataset types

Choose a dataset type based on your task. This determines your annotation format, model output, and evaluation metrics.

Training, evaluation, and inference

Understand how Datature Vi trains models, what the settings mean, how to assess results, and how inference generates output.

When you pick a base model by name (for example Qwen3.5 4B vs 9B, or NVILA-Lite for tight memory), the smaller option is usually the right first experiment: lower cost and faster runs while you prove the workflow. Move up when metrics stop improving or when deployment constraints point to a specific architecture. Compare options in Model architectures.

Deployment and resources

Advanced capabilities

Reference

Next steps