vania-janet/multiturn-rag-retrieval-data huggingface dataset:
This documentation for the classic Watson Assistant experience has moved. For the most up-to-date version, see [Algorithm version and training](https://cloud.ibm.com/docs/watson-assistant?topic=watson-assistant-algorithm-version). To see all documentation for the new Watson Assistant, please go [here](https://cloud.ibm.com/docs/watson-assistant).



 Algorithm version and training 

The Algorithm version setting allows you to choose which Watson Assistant algorithm is used for training. Your assistant is trained when you update the content or settings, or through [automatic retraining](https://cloud.ibm.com/docs/assistant?topic=assistant-algorithm-versionalgorithm-version-auto-retrain).

There are three choices:



* Beta: Use this to preview and test what is coming. The capability in the beta version at any given time is likely to become a supported version later on. It's not recommended to use the beta version in a production deployment.
* Latest: The current supported version that's recommended for your live production assistant.
* Previous: The last supported before the latest version. Support for this version ends when the next version is released.



To choose an algorithm version for actions:



1. On the Actions page, click Global settings![Gear icon](https://cloud.ibm.com/docs-content/v1/content/icons/settings.svg).
2. Click the Algorithm Version tab.
3. Choose a version, then click Save.



To choose an algorithm version for dialog:



1. On the Dialog page, open the Options section.
2. Click Algorithm Version.
3. Choose a version.



The latest and previous versions have date labels such as Latest (01-Jun-2022) or Previous (01-Jan-2022). See the [Watson Assistant release notes](https://cloud.ibm.com/docs/watson-assistant?topic=watson-assistant-watson-assistant-release-notes) for details about each algorithm version release.
Before you begin, make sure that you have the [Operator or Administrator IBM Cloud IAM platform access role](https://cloud.ibm.com/docs/containers?topic=containers-userschecking-perms).

To update the Kubernetes master major or minor version:



1. Review the [Kubernetes changes](https://cloud.ibm.com/docs/containers?topic=containers-cs_versions) and make any updates marked Update before master.
2. Review any [Kubernetes helpful warnings](https://kubernetes.io/blog/2020/09/03/warnings/), such as deprecation notices.
3. Check the add-ons and plug-ins that are installed in your cluster for any impact that might be caused by updating the cluster version.



* Checking add-ons



1. List the add-ons in the cluster.

ibmcloud ks cluster addon ls --cluster CLUSTER
2. Check the supported Kubernetes version for each add-on that is installed.

ibmcloud ks addon-versions
3. If the add-on must be updated to run in the Kubernetes version that you want to update your cluster to, [update the add-on](https://cloud.ibm.com/docs/containers?topic=containers-managed-addonsupdating-managed-add-ons).



* Checking plug-ins



1. In the [Helm catalog](https://cloud.ibm.com/kubernetes/helm), find the plug-ins that you installed in your cluster.
2. From the side menu, expand the SOURCES & TAR FILE section.
3. Download and open the source code.
4. Check the README.md or RELEASENOTES.md files for supported versions.
5. If the plug-in must be updated to run in the Kubernetes version that you want to update your cluster to, update the plug-in by following the plug-in instructions.





4.
On the left pane, click +New.
2. Set the name to iris_model.
3. Under Watson Machine Learning service instance, select the service previously associated.



3. Click Create.



Once the model is created,



1. Add training data by clicking Select data from project.



1. Choose the iris_initial.csv file under Data asset.
2. Click Select asset.



2. If prompted, answer No to Create a time series forecast?.
3. Select Species as your What do you want to predict?.
4. Click Experiment settings.
5. Select Data source.
6. Under Training data split, set Holdout data split to 15% by moving the slider.
7. On the left menu, Click on Prediction:



1. Set Prediction type to Multiclass classification.
2. Set Optimized metric as Accuracy.
3. Click on Save settings.



8. Click on Run experiment.
9. The AutoAI experiment may take up to 5 minutes to select the right Algorithm for your model. Click on Swap view to see the Relationship map.

Each model pipeline is scored for a variety of metrics and then ranked. The default ranking metric for binary classification models is the area under the ROC curve, for multi-class classification models is accuracy, and for for regression models is the root mean-squared error (RMSE). The highest-ranked pipelines are displayed in a leaderboard, so you can view more information about them. The leaderboard also provides the option to save select model pipelines after reviewing them.



Once the experiment completes running,



1. Click on Pipeline comparison to view how the top pipelines compare.
2. Sort the leaderboard by a different metric by clicking on the various column headers.
3. Click a pipeline to view more detail about the metrics and performance.

Sorting by different metrics may not change the leaderboard rankings as the dataset used in this tutorial is very simple and used only for your understanding of the concepts.
You can update key-value pairs for your defined configmaps from the console in one of the following ways.



* Go to the Secrets and configmaps page for your project and locate the configmap that you want to update. Click the name of the configmap that you want to update to open it.
* If your configmap is referenced by an app or job, then use the links in the environment variables table on the Environmental variables tab of your app or job. These links take you directly to your configmap.



2. Click Edit and make the updates for your configmap.
3. Click Save to save the changes to your configmap.



If your updated configmap is referenced by a job or app, then your job or app must be restarted for the new data to take effect.



* Apps - From the page for your app, click New revision and then Save and deploy. Alternatively, you can wait for your app to scale to zero and when the app scales up, the app uses the updated configmap.
* Jobs - From the page for your job, click Submit job to run your job, or you can rerun a job. This new job run uses the updated configmap.







 Updating configmaps with the CLI 

You can update an existing configmap and its key-value pairs with the CLI.



1. To change the value of a key-value pair in a configmap, use the [configmap update](https://cloud.ibm.com/docs/codeengine?topic=codeengine-clicli-configmap-update) command. Let's update the myliteralconfigmap configmap to change the value of the TARGET key from Sunshine to Stranger.

ibmcloud ce configmap update --name myliteralconfigmap --from-literal "TARGET=Stranger"
2. Now that your configmap is updated, use the configmap get command to display details about a specific configmap. For example,
How do I update my Terraform version? 

If you try to validate a configuration, and the validation fails due to a plan failure, you might need to edit the settings in your IBM Cloud® Schematics workspace.

  What’s happening 

You tried to validate a configuration, but the validation failed due to a plan failure. In the Schematics logs, you see the following error:

> Error: Unsupported Terraform Core version

  Why it’s happening 

Schematics is using a version of Terraform that is no longer supported.

  How to fix it 

Update the settings of the Schematics workspace to use a supported Terraform version.

Complete the following steps:



1.  From the projects list, select a project.
2.  Go to the Configurations tab, and select a deployable architecture configuration.
3.  Select the Workspace URL to open the Schematics logs.
4.  Click Settings.
5.  In the Details section, select Update for the Offering version to update the Terraform version that your workspace is using.
[Adding documents to a workspace](https://cloud.ibm.com/docs/watson-knowledge-studio-data?topic=watson-knowledge-studio-data-exportimportwks_exportimport_expimp_doc)



10. From Knowledge Studio, click Train to retrain the model.

For more information, see the appropriate topic for your deployment type:



IBM Cloud

[Training the machine learning model](https://cloud.ibm.com/docs/watson-knowledge-studio?topic=watson-knowledge-studio-train-ml)
IBM Cloud Pak for Data

[Training the machine learning model](https://cloud.ibm.com/docs/watson-knowledge-studio-data?topic=watson-knowledge-studio-data-train-ml)



11. Now, you're ready to export the model from Knowledge Studio and import it to Discovery to use the model as a machine learning enrichment.

For more information, see [Using imported ML models to find custom terms](https://cloud.ibm.com/docs/discovery-data?topic=discovery-data-domain-ml).
For more information about the required and optional request parameters, check out the [API reference](https://cloud.ibm.com/apidocs/secrets-manager/secrets-manager-v2).





 Updating secret versions metadata in the UI 

You can update the metadata of a specific version of a secret by using the Secrets Manager UI.

To update the metadata of a secret version, complete the following steps.



1. In the console, click the Menu icon ![Menu icon](https://cloud.ibm.com/docs-content/v1/content/991f60ddbe3ab0a9aa69481a07a0a73b359fa329/icons/icon_hamburger.svg)> Resource List.
2. From the list of services, select your instance of Secrets Manager.
3. In the Secrets Manager UI, go to your Secrets list.
4. In the row for the secret that you want to inspect, click the Actions menu ![Actions icon](https://cloud.ibm.com/docs-content/v1/content/991f60ddbe3ab0a9aa69481a07a0a73b359fa329/icons/actions-icon-vertical.svg)> Version history.

If the secret was rotated previously, the page displays information about the current and previous versions, for example the date that each version was created. Up to 50 versions can be listed for a secret.
5. Upload a file or enter the metadata and the version metadata in JSON format.
6. Click Update.







 Updating secret versions metadata from the CLI 

You can use the Secrets Manager CLI plug-in to update the metadata of a specific version of a secret.

To update the metadata of a secret, run the [ibmcloud secrets-manager secret-metadata-update](https://cloud.ibm.com/docs/secrets-manager?topic=secrets-manager-cli-plugin-secrets-manager-clisecrets-manager-cli-secret-metadata-update-command) command.
Algorithm version and training 

The Algorithm version setting allows you to choose which Watson Assistant algorithm is used for training. Your assistant is trained when you update the content or settings, or through [automatic retraining](https://cloud.ibm.com/docs/watson-assistant?topic=watson-assistant-algorithm-versionalgorithm-version-auto-retrain).

There are three choices:



* Beta: Use this to preview and test what is coming. The capability in the beta version at any given time is likely to become a supported version later on. It's not recommended to use the beta version in a production deployment.
* Latest: The current supported version that's recommended for your live production assistant.
* Previous: The last supported before the latest version. Support for this version ends when the next version is released.



To choose an algorithm version for actions:



1. On the Actions page, click Global settings![Gear icon](https://cloud.ibm.com/docs-content/v1/content/icons/settings.svg).
2. Click the Algorithm Version tab.
3. Choose a version, then click Save.



To choose an algorithm version for dialog:



1. On the Dialog page, open the Options section.
2. Click Algorithm Version.
3. Choose a version.



The latest and previous versions have date labels such as Latest (01-Jun-2022) or Previous (01-Jan-2022). See the [Watson Assistant release notes](https://cloud.ibm.com/docs/watson-assistant?topic=watson-assistant-watson-assistant-release-notes) for details about each algorithm version release.

Algorithm version choices are currently available for Arabic, Chinese (Simplified), Chinese (Traditional), Czech, Dutch, English, French, German, Japanese, Korean, Italian, Portuguese, and Spanish. The universal language model uses a default algorithm.



 Automatic retraining 

IBM Cloud

Watson Assistant was released as a service in July 2016. Since then, users have been creating and updating skills to meet their virtual assistant needs.
This version is then labeled as Previous after you upgrade. In addition, each new release will support running models that were trained on the version prior to that so that upgrading won't impact your runtime. For example, if you upgrade from IBM Cloud Pak for Data 4.6 to 4.7, and were using Latest (01-Jun-2022) that version becomes listed as Previous (01 June 2022) and remains your selected version.



 Automatic retraining after upgrading 

After your Watson Assistant for IBM Cloud Pak for Data upgrade is complete, Watson Assistant performs automatic retraining for any assistant models that were trained using a version that is no longer supported. In this case, Watson Assistant automatically retrains your assistant to the Latest version. This automatic retraining is required to assure your ability to run your trained models in your next upgrade.





 Best practices 

It's recommended to use the Latest version in your production deployment of Watson Assistant for IBM Cloud Pak for Data. This is the default for newly-created assistants. During an upgrade, your settings don't automatically switch existing assistants to use the latest version. If prior to your upgrade you had selected Latest, your settings continue to use that version, now labeled as Previous. After you upgrade, it's recommended you choose Latest and run basic regression tests.

IBM performs robust testing on a variety of data sets to minimize impacts on existing assistants. But given the nature of machine learning models and the nuance and subtlety of natural languages processing, you may find some discrepancies from version to version. If you find a major issue through your tests, you have the ability to switch your settings and use Previous to return to the prior behavior. In this event, we recommend you contact IBM and provide details of your test so that that IBM can support you in the steps to resolve the problem.

It's also recommended that you try the Beta version in one of your test systems after you upgrade. This gives you early visibility to changes that are likely to be delivered in a future version, and will reduce the probability of negative impacts to your production systems. IBM values both positive and negative feedback from customers who use Beta. You will have the opportunity to shape how the algorithms function before the version is promoted to Latest in a future version.
For more information, see the [IBM Cloud Pak for Data Software Support Lifecycle Addendum](https://www.ibm.com/support/pages/ibm-cloud-pak-data-software-support-lifecycle-addendum)

Each new release includes full support for the version listed as Latest in the most recent prior release. This version is then labeled as Previous after you upgrade. In addition, each new release will support running models that were trained on the version prior to that so that upgrading won't impact your runtime. For example, if you upgrade from IBM Cloud Pak for Data 4.6 to 4.7, and were using Latest (01-Jun-2022) that version becomes listed as Previous (01 June 2022) and remains your selected version.



 Automatic retraining after upgrading 

After your Watson Assistant for IBM Cloud Pak for Data upgrade is complete, Watson Assistant performs automatic retraining for any assistant models that were trained using a version that is no longer supported. In this case, Watson Assistant automatically retrains your assistant to the Latest version. This automatic retraining is required to assure your ability to run your trained models in your next upgrade.





 Best practices 

It's recommended to use the Latest version in your production deployment of Watson Assistant for IBM Cloud Pak for Data. This is the default for newly-created assistants. During an upgrade, your settings don't automatically switch existing assistants to use the latest version. If prior to your upgrade you had selected Latest, your settings continue to use that version, now labeled as Previous. After you upgrade, it's recommended you choose Latest and run basic regression tests.

IBM performs robust testing on a variety of data sets to minimize impacts on existing assistants. But given the nature of machine learning models and the nuance and subtlety of natural languages processing, you may find some discrepancies from version to version. If you find a major issue through your tests, you have the ability to switch your settings and use Previous to return to the prior behavior. In this event, we recommend you contact IBM and provide details of your test so that that IBM can support you in the steps to resolve the problem.

It's also recommended that you try the Beta version in one of your test systems after you upgrade.
Updates to English next-generation telephony models
: The English next-generation telephony models have been updated for improved speech recognition:



* en-AU_Telephony
* en-GB_Telephony
* en-IN_Telephony
* en-US_Telephony



All of these models continue to support low latency. You do not need to upgrade custom models that are based on these models. For more information about all available next-generation models, see [Next-generation languages and models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-models-ng).

Italian next-generation multimedia model now supports low latency
: The Italian next-generation multimedia model, it-IT_Multimedia, now supports low latency. For more information about next-generation models and low latency, see



* [Next-generation languages and models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-models-ng)
* [Customization support for next-generation models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-custom-supportcustom-language-support-ng)
* [Low latency](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-interimlow-latency)



Troubleshooting upgrade from version 4.0.x to version 4.5.x
: When you upgrade the Speech services from version 4.0.x to version 4.5.x, you might encounter an issue where the PostgreSQL pods become stuck in the Terminating state. If this problem occurs during your upgrade, perform the following steps to resolve the problem. The information and steps are also documented in Upgrading Watson Speech services from Version 4.0 to Version 4.5 in the Upgrading topic of [Watson Speech services on IBM Cloud Pak for Data](https://www.ibm.com/docs/en/cloud-paks/cp-data/4.5.x?topic=services-watson-speech).
},
{
"type": "locatedAt",
"sentence": "Vehicle 1, a 1995 Honda Civic was traveling north
on a two lane undivided roadway, negotiating a curve to
the left on an upgrade.",
"score": 0.40592,
"arguments":
{
"text": "negotiating",
"entities":
{
"type": "EventMeeting",
"text": "negotiating"
}
]
},
{
"text": "roadway",
"entities":
{
"type": "Facility",
"text": "roadway"
}
]
}
]
}
],
"language": "en"
}
Show more



For more information, see the [Natural Language Understanding documentation](https://cloud.ibm.com/docs/natural-language-understanding).







 Deploying the same model version to multiple services 

If you wish to deploy a specific version of the same machine learning model to multiple IBM Watson service instances, navigate to the Versions page and click the Deploy link on the row of the version that you want to deploy to an additional service.





 Undeploying models 

If you want to undeploy a model or find a model ID, view the Deployed Models page.



 Procedure 

To undeploy models or find model IDs:



1. Launch Knowledge Studio.
2. From the Settings menu in the top right menu bar, select Manage deployed models.
3. From the list of deployed models, find the model you want to view or undeploy.
4. To undeploy the model, from the last column of that row, click Undeploy model.
5. To find the model ID, see the Model ID column.



Alternatively, you can undeploy models from the Versions pages for rule-based models and machine learning models.







 Deleting a version
* If you use the default values for these parameters, continue to do so after the upgrade. The default values will likely continue to offer the best results for speech recognition.
* If you specify non-default values for these parameters, experiment with the default values following upgrade. Your custom model might work well for speech recognition with the default values.



If you feel that using different values for these parameters might improve speech recognition with your custom model, experiment with incremental changes to determine whether the parameters are needed to improve speech recognition.

Note: At this time, the improvements to language model customization apply only to custom models that are based on the next-generation English or Japanese base language models listed earlier. Over time, the improvements will be made available for other next-generation language models.

More information: For more information about upgrading and about speech recognition with these parameters, see



* [Improved language model customization for next-generation models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-customizationcustomLanguage-intro-ng)
* [Upgrading custom models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-custom-upgrade)
* [Using customization weight](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-languageUseweight)
* [Character insertion bias](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-parsinginsertion-bias)



New environment variable for Speech services custom resource
: The documentation now includes instructions to create an environment variable named ${CUSTOM_RESOURCE_SPEECH}. You append the new variable to the cpd_vars.sh script, and source the script to use the variable in your environment. For more information, see Information you need to complete this task in [Installing Watson Speech services](https://www.ibm.com/docs/en/cloud-paks/cp-data/4.6.x?topic=services-installing), or refer to any of the upgrade topics for the Speech services.
Applying queue manager updates 

Perform an update of a running queue manager.



  Prerequisites 

An existing queue manager that has a pending version or revision update.

--------------------





  Perform update 

Note: Updating your queue manager is non-reversible and will involve it being offline for a few minutes.



1.  Log in to the IBM Cloud console.
2.  Click on the 'hamburger menu'.
3.  Click Dashboard.
4.  Locate and click on your IBM MQ service instance, found under the 'Services' heading.
5.  From the list of your queue managers, click on the one you want to update.
6.  Click Update now and review the contents of the update.
7.  Click Continue. ![Image showing the queue manager update button](https://cloud.ibm.com/docs-content/v1/content/3a120b16db5fdc3e70d071af8a63ce5515c1b096/mqcloud/images/mqoc_qm_update_qm3.png)
8.  The update will take a few minutes to complete during which time the queue manager details view will show a status of Updating. ![Image showing the queue manager being updated](https://cloud.ibm.com/docs-content/v1/content/3a120b16db5fdc3e70d071af8a63ce5515c1b096/mqcloud/images/mqoc_qm_update_qm6.png)
9.  When the update has completed, the queue manager details view will show a status of Running as well as the updated version/revision and when it was last updated. ![Image showing the updated queue manager](https://cloud.ibm.com/docs-content/v1/content/3a120b16db5fdc3e70d071af8a63ce5515c1b096/mqcloud/images/mqoc_qm_update_qm7.png)