Model lifecycle
Language service features utilize AI models that are versioned. We update the language service with new model versions to improve accuracy, support, and quality. As models become older, they are retired. Use this article for information on that process, and what you can expect for your applications.
Prebuilt features
Expiration timeline
Our standard (not customized) language service features are built upon AI models that we call pre-trained models. We update the language service with new model versions every few months to improve model accuracy, support, and quality.
As new models and functionalities become available, older less accurate models are deprecated. To ensure you are using the latest model version and avoid interruptions to your applications, we highly recommend using the default model-version parameter (latest) in your API calls. After their deprecation date, pre-built model versions will no longer be functional and your implementation may be broken.
Stable (not preview) model versions are deprecated six months after the release of another stable model version. Features in preview don't maintain a minimum retirement period and may be deprecated at any time.
Choose the model-version used on your data
By default, API requests will use the latest Generally Available model. You can use an optional parameter to select the version of the model to be used (not recommended).
Tip
If you’re using the SDK for C#, Java, JavaScript or Python, see the reference documentation for information on the appropriate model-version parameter.
For synchronous endpoints, use the model-version query parameter. For example:
POST <your-language-resource-endpoint>/language/:analyze-text?api-version=2022-05-01&model-version=2022-06-01.
For asynchronous endpoints, use the model-version property in the request body under task properties.
The model-version used in your API request will be included in the response object.
Note
If you are using an model version that is not listed in the table, then it was subjected to the expiration policy.
Use the table below to find which model versions are supported by each feature:
| Feature | Supported versions | Model versions to be deprecated |
|---|---|---|
| Sentiment Analysis and opinion mining | 2021-10-01, 2022-06-01* |
2019-10-01, 2020-04-01 |
| Language Detection | 2021-11-20* |
2019-10-01, 2020-07-01, 2020-09-01, 2021-01-05 |
| Entity Linking | 2021-06-01* |
2019-10-01, 2020-02-01 |
| Named Entity Recognition (NER) | 2021-06-01* |
2019-10-01, 2020-02-01, 2020-04-01, 2021-01-15 |
| Personally Identifiable Information (PII) detection | 2020-07-01, 2021-01-15* |
2019-10-01, 2020-02-01, 2020-04-01, 2020-07-01 |
| PII detection for conversations (Preview) | 2022-05-15-preview** |
|
| Question answering | 2021-10-01* |
|
| Text Analytics for health | 2021-05-15, 2022-03-01* |
|
| Key phrase extraction | 2021-06-01, 2022-07-01* |
2019-10-01, 2020-07-01 |
| Document summarization (preview) | 2021-08-01* |
|
| Conversation summarization (preview) | 2022-05-15-preview** |
* Latest Generally Available (GA) model version
** Latest preview version
Important
The versions listed for deprecation will be unavailable for use after October 30, 2022.
Custom features
Expiration timeline
As new training configs and new functionality become available; older and less accurate configs are retired, see the following timelines for configs expiration:
New configs are being released every few months. So, training configs expiration of any publicly available config is six months after its release. If you have assigned a trained model to a deployment, this deployment expires after twelve months from the training config expiration.
After training config version expires, API calls will return an error when called or used if called with an expired config version. By default, training requests will use the latest available training config version. To change the config version, use trainingConfigVersion when submitting a training job and assign the version you want.
Tip
It's recommended to use the latest supported config version
You can train and deploy a custom AI model from the date of training config version release, up until the Training config expiration date. After this date, you will have to use another supported training config version for submitting any training or deployment jobs.
Deployment expiration is when your deployed model will be unavailable to be used for prediction.
Use the table below to find which model versions are supported by each feature:
| Feature | Supported Training config versions | Training config expiration | Deployment expiration |
|---|---|---|---|
| Custom text classification | 2022-05-01 |
10/28/2022 |
10/28/2023 |
| Conversational language understanding | 2022-05-01 |
10/28/2022 |
10/28/2023 |
| Custom named entity recognition | 2022-05-01 |
10/28/2022 |
10/28/2023 |
| Orchestration workflow | 2022-05-01 |
10/28/2022 |
10/28/2023 |
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. It's recommended to use the latest available API versions.
If you are using the Language Studio for building your project you will be using the latest API version available. If you need to use another API version this is only available directly through APIs.
Use the table below to find which API versions are supported by each feature:
| Feature | Supported versions | Latest Generally Available version | Latest preview version |
|---|---|---|---|
| Custom text classification | 2022-05-01 |
2022-05-01 |
|
| Conversational language understanding | 2022-05-01 |
2022-05-01 |
|
| Custom named entity recognition | 2022-05-01 |
2022-05-01 |
|
| Orchestration workflow | 2022-05-01 |
2022-05-01 |
Next steps
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