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Every context is served by a model. Until you train one, contexts use Automatic: NSL’s shared base models, which work from your item descriptions and general behaviour patterns. Training a model on your own events is how recommendations become specific to your users. Open Models → Models. The page has two tabs: Catalog to choose what to train, and Your models to manage what you have trained.

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The flow

Retraining never overwrites a model in place. Each run produces a new version, which only replaces the serving version if you deploy it or it passes the promotion gate. See Recommendation architectures for what each catalogue model does and how to compare candidates.

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Catalog

The catalogue lists every recommender you can train, with how well it performs, what it costs and whether your data is ready for it. Use the search box, Filter by architecture family, and Rank by scenario (for example cold start or long sessions) to narrow the list. Labels mark New and Generative recommenders; Baseline entries are benchmark references and cannot be selected. Select a recommender to see:
  • Scenario performance: how it ranks against the others in each situation a recommender has to handle.
  • Benchmark dimensions: reference scores across public datasets under a shared protocol; check the dataset coverage and caveats for each entry.
  • Data it needs: the signals and volume it requires.
  • Potential use cases.
  • Training and serving: training compute, serving mode, monthly serving cost, inference cost per 1,000 requests, model size and reference latency.

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Train

1

Select recommenders

Tick one or more recommenders. Each is trained as an independent job against one shared snapshot of your data, so their results are comparable.
2

Continue to training data

Choose the signal template to train from, and check readiness and estimated cost.
3

Start training

Follow progress in Model Training. You are notified when runs finish.
Training your own models needs the Pro plan or above. Recommenders your plan cannot train show Upgrade to train this model.

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Your models

Each trained model shows whether it is Live, its version, deployment state (Serving, Packaged, not deployed or No package yet), the training data it used and whether it is up to date. Platform base models lists the shared models NSL publishes, which serve contexts set to Automatic. Select a model to open its details: Rename a model from its header to give it a label your team recognises. The system name, built from the template and run, is kept for traceability.

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Deploy a model

Deploying means choosing the model for a context. Open the context in Contexts, and on Details pick it from Model under Your trained models. The model is deployed for that context and starts serving once it is ready; until then, the context keeps its previous model. To roll back, choose the previous model or Automatic in the same place.

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Scheduled retraining

Open a model and turn on Retrain on a schedule to keep it current automatically. The panel shows the next and last run, and each run’s outcome: Promoted, Kept the current version or Skipped. A model whose signal template has been deleted cannot be retrained.

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Model families

Each model belongs to a family that describes its job, such as general recommendations, similar items, trending or email digest. Open Model families from the Models page to see, for each family:
  • the base models NSL publishes, any of which can seed your own fine-tune;
  • your fine-tuned versions, the newest ready one of which overrides the base for your workspace;
  • the architectures available in the family.
When a context serves from a family, NSL uses your latest ready fine-tune, then the family’s base model, then the general base model if nothing in the family is ready.