Model lifecycle

Language service features utilize AI models. We update the language service with new model versions to improve accuracy, support, and quality. As models become older, they're retired. Use this article for information on that process, and what you can expect for your applications.

Prebuilt features

Our standard (not customized) language service features are built on AI models that we call pretrained or prebuilt models.

We regularly update the language service with new model versions to improve model accuracy, support, and quality.

By default, all API requests use the latest Generally Available (GA) model.

Choose the model-version used on your data

We recommend using the latest model version to utilize the latest and highest quality models. As our models improve, it's possible that some of your model results may change. Model versions may be deprecated, so we no longer accept specified GA model versions in your implementation.

Preview models used for preview features don't maintain a minimum retirement period and may be deprecated at any time.

By default, API and SDK requests use the latest Generally Available model. To use a model in preview, you can use an optional parameter modelVersion to select the preview version of the model to be used (not recommended for GA models).

Use the following table to find which model versions support each feature:

Custom features

Expiration timeline

For custom features, there are two key parts of the AI implementation: training and deployment. New configurations are released regularly with regular AI improvements, so older and less accurate configurations are retired.

Use the following table to find which model versions support each feature:

** For latest training configuration versions, the posted expiration dates are subject to availability of a newer model version. If no newer model versions are available, the expiration date may be extended.

Training configurations are typically available for six months after its release. If you assigned a trained configuration to a deployment, this deployment expires after twelve months from the training config expiration. If your models are about to expire, you can retrain and redeploy your models with the latest training configuration version.

After the training config expiration date, you'll have to use another supported training configuration version to submit any training or deployment jobs. After the deployment expiration date, your deployed model will be unavailable to be used for prediction.

After training config version expires, API calls will return an error when called or used if called with an expired configuration version. By default, training requests use the latest available training configuration version. To change the configuration version, use the trainingConfigVersion parameter when submitting a training job and assign the version you want.

API versions

When you're making API calls to the following features, you need to specify the API-VERISON you want to use to complete your request. We recommend that you use the latest available API version.

If you're using Language Studio for your projects, you use the latest API version available. Other API versions are only available through the REST APIs and client libraries.

Use the following table to find which API versions support each feature:

Next steps

Azure AI Language overview