How do I sign up for watsonx?

Go to Try IBM watsonx.ai or Try watsonx.governance. If you sign up for watsonx.governance, you automatically provision watsonx.ai as well.

Can I try watsonx for free?

Yes, when you sign up for IBM watsonx.ai, you automatically provision the free version of the underlying services: watsonx.ai Studio, watsonx.ai Runtime, and IBM Cloud Object Storage. When you sign up for IBM watsonx.governance, you automatically provision the free version of Watson OpenScale and the free versions of the services for IBM watsonx.ai.

How do I upgrade watsonx.ai and watsonx.governance?

When you're ready to upgrade any of the underlying services for watsonx.ai or watsonx.governance, you can upgrade in place without losing any of your work or data.

You must be the owner or administrator of the IBM Cloud account for a service to upgrade it. See Upgrading services on watsonx.

What happened to my Watson Studio and Watson Machine Learning services?

The Watson Studio service is now named watsonx.ai Studio. The Watson Machine Learning service is now named watsonx.ai Runtime.

How can I get the most runtime from my watsonx.ai Studio Lite plan?

The watsonx.ai Studio Lite plan allows for 10 CUH per month. You can maximize your available CUH by setting your assets to use environments with lower CUH rates. For example, you can change your notebook environment. To see the available environments and the required CUH, go to the Services catalog page for watsonx.ai Studio.

How do I find my IBM Cloud account owner?

If you have an enterprise account or work in an IBM Cloud that you don't own, you might need to ask an account owner to give you access to a workspace or another role.

To find your IBM Cloud account owner:

1. From the navigation menu, choose Administration > Access (IAM).
2. From the avatar menu, make sure you're in the right account, or switch accounts, if necessary.
3. Click Users, and find the username with the word owner next to it.

To understand roles, see Roles for IBM watsonx. To determine your roles, see Determine your roles.

Can I provide feedback?

Yes, we encourage feedback as we continue to develop this platform. From the navigation menu, select Support > Share an idea.

What foundation models are available and where do they come from?

See the complete list of supported foundation models.

What data was used to train foundation models?

Links to details about each model, including pretraining data and fine-tuning, are available here: Supported foundation models.

Do I need to check generated output for biased, inappropriate, or incorrect content?

Yes, you must review the generated output of foundation models. Third Party models have been trained with data that might contain biases and inaccuracies and can generate outputs containing misinformation, obscene or offensive language, or discriminatory content.

In the Prompt Lab, when you toggle AI guardrails on, any sentence in the prompt text or model output that contains harmful language will be replaced with a message saying potentially harmful text has been removed.

See Avoiding undesirable output.

Which languages can I generate output for?

The languages that each model supports varies. Some models support only the English language. Other models support multiple languages.

See Choosing a foundation model.

Is there a limit to how much text generation I can do?

With the free trial of watsonx.ai, you can use up to 25,000 tokens per month. Your token usage is the sum of your input and output tokens.

With a paid service plan, there is no token limit, but you are charged for the tokens that you submit as input plus the tokens that you receive in the generated output.

See watsonx.ai Runtime plans.

Does prompt engineering train the foundation model?

No, submitting prompts to a foundation model does not train the model. The models available in watsonx.ai are pretrained, so you do not need to train the models before you use them.

See Prompt tips.

Does IBM have access to or use my data in any way?

No, IBM does not have access to your data.

Your work on watsonx.ai, including your data and the models that you create, are private to your account:

• Your data is accessible only by you. Your data is used to train only your models. Your data will never be accessible or used by IBM or any other person or organization. Your data is stored in dedicated storage buckets and is encrypted at rest and in motion.
• Your models are accessible only by you. Your models will never be accessible or used by IBM or any other person or organization. Your models are secured in the same way as your data.

Learn more about security and your options:

• Security and privacy of foundation models
• Security for IBM watsonx.ai
• Data security

What APIs are available?

You can prompt foundation models in watsonx.ai programmatically by using the watsonx.ai REST API, Python library, or the Node.js SDK.

See the following topics for more details:

• REST API
• Python library
• Node.js SDK

How do I load very large files to my project?

You can't load data files larger than 5 GB to your project. If your files are larger, you must use the Cloud Object Storage API and load the data in multiple parts. See the curl commands for working with Cloud Object Storage directly on IBM Cloud.

See Adding very large objects to a project's Cloud Object Storage.

What is saved in IBM Cloud Object Storage for workspaces?

When you create a project or deployment space, you specify a IBM Cloud Object Storage and create a bucket that is dedicated to that workspace. These types of objects are stored in the IBM Cloud Object Storage bucket for the workspace:

• Files for data assets that you uploaded into the workspace.
• Files associated with assets that run in tools, such as, notebooks and models.
• Metadata about assets, such as the asset type, format, and tags.

Do I need to upgrade IBM Cloud Object Storage when I upgrade other services?

You must upgrade your IBM Cloud Object Storage instance only when you run out of storage space. Other services can use any IBM Cloud Object Storage plan and you can upgrade any service or your IBM Cloud Object Storage service independently.

Why am I unable to add storage to an existing project or to see the IBM Cloud Object Storage selection in the New Project dialog?

IBM Cloud Object Storage requires an extra step for users who do not have administrative privileges for it. The account administrator must enable nonadministrative users to create projects.

If you have administrator privileges and do not see the latest IBM Cloud Object Storage, try again later because server-side caching might cause a delay in rendering the latest values.

Can I install libraries or packages to use in my notebooks?

You can install Python libraries and R packages through a notebook, and those libraries and packages will be available to all your notebooks that use the same environment template. For instructions, see Import custom or third-party libraries. If you get an error about missing operating system dependencies when you install a library or package, notify IBM Support. To see the preinstalled libraries and packages and the libraries and packages that you installed, from within a notebook, run the appropriate command:

• Python: !pip list
• R: installed.packages()

Can I call functions that are defined in one notebook from another notebook?

There is no way to call one notebook from another notebook on the platform. However, you can put your common code into a library outside of the platform and then install it.

Can I add arbitrary notebook extensions?

No, you can't extend your notebook capabilities by adding arbitrary extensions as a customization because all notebook extensions must be preinstalled.

How do I access the data from a CSV file in a notebook?

After you load a CSV file into object storage, load the data by clicking the Code snippets icon in an opened notebook, clicking Read data and selecting the CSV file from the project. Then, click in an empty code cell in your notebook and insert the generated code.

How do I access the data from a compressed file in a notebook?

After you load the compressed file to object storage, get the file credentials by clicking the Code snippets icon in an opened notebook, clicking Read data and selecting the compressed file from the project. Then, click in an empty code cell in your notebook and load the credentials to the cell. Alternatively, click to copy the credentials to the clipboard and paste them into your notebook.

How secure is IBM watsonx?

The IBM watsonx platform is very secure and resilient. See Security of IBM watsonx.

Is my data and notebook protected from sharing outside of my collaborators?

The data that is loaded into your project and notebooks is secure. Only the collaborators in your project can access your data or notebooks. Each platform account acts as a separate tenant of the Spark and IBM Cloud Object Storage services. Tenants cannot access other tenant's data.

If you want to share your notebook with the public, then hide your data service credentials in your notebook. For the Python and R languages, enter the following syntax: # @hidden_cell

Be sure to save your notebook immediately after you enter the syntax to hide cells with sensitive data.

Only then should you share your work.

Do I need to back up my notebooks?

No. Your notebooks are stored in IBM Cloud Object Storage, which provides resiliency against outages.

What are the implications of sharing a notebook?

When you share a notebook, the permalink never changes. Any person with the link can view your notebook. You can stop sharing the notebook by clearing the checkbox to share it. Updates are not automatically shared. When you update your notebook, you can sync the shared notebook by reselecting the checkbox to share it.

How can I share my work outside of RStudio?

One way of sharing your work outside of RStudio is connecting it to a shared GitHub repository that you and your collaborators can work from. Read this blog post for more information.

However, the best method to share your work with the members of a project is to use notebooks in the project that uses the R kernel.

RStudio is a great environment to work in for prototyping and working individually on R projects, but it is not yet integrated with projects.

How do I share my SPSS Modeler flow with another project?

By design, modeler flows can be used only in the project where the flow is created or imported. If you need to use a modeler flow in a different project, you must download the flow from current project (source project) to your local environment and then import the flow to another project (target project).

How do I run an AutoAI experiment?

Go to Creating an AutoAI experiment from sample data to watch a short video to see how to create and run an AutoAI experiment and then follow a tutorial to set up your own sample.

What is available for automated model building?

The AutoAI graphical tool automatically analyzes your data and generates candidate model pipelines that are customized for your predictive modeling problem.  These model pipelines are created iteratively as AutoAI analyzes your data set and discovers data transformations, algorithms, and parameter settings that work best for your problem setting.  Results are displayed on a leaderboard, showing the automatically generated model pipelines ranked according to your problem optimization objective. For details, see AutoAI overview.

What frameworks and libraries are supported for my machine learning models?

You can use popular tools, libraries, and frameworks to train and deploy machine learning models by using IBM watsonx.ai Runtime. The supported frameworks topic lists supported versions and features, as well as deprecated versions scheduled to be discontinued.

What is an API Key?

API keys allow you to easily authenticate when using the CLI or APIs that can be used across multiple services. API Keys are considered confidential since they are used to grant access. Treat all API keys as you would a password since anyone with your API key can impersonate your service.

What is Watson OpenScale

IBM Watson OpenScale tracks and measures outcomes from your AI models, and helps ensure they remain fair, explainable, and compliant wherever your models were built or are running. Watson OpenScale also detects and helps correct the drift in accuracy when an AI model is in production

How do I convert a prediction column from an integer data type to a categorical data type?

For fairness monitoring, the prediction column allows only an integer numerical value even though the prediction label is categorical. How do I configure a categorical feature that is not an integer? Is a manual conversion required?

The training data might have class labels such as “Loan Denied”, “Loan Granted”. The prediction value that is returned by IBM watsonx.ai Runtime scoring end point has values such as “0.0”, “1.0". The scoring end point also has an optional column that contains the text representation of prediction. For example, if prediction=1.0, the predictionLabel column might have a value “Loan Granted”. If such a column is available, when you configure the favorable and unfavorable outcome for the model, specify the string values “Loan Granted” and “Loan Denied”. If such a column is not available, then you need to specify the integer and double values of 1.0, 0.0 for the favorable, and unfavorable classes.

IBM watsonx.ai Runtime has a concept of output schema that defines the schema of the output of IBM watsonx.ai Runtime scoring end point and the role for the different columns. The roles are used to identify which column contains the prediction value, which column contains the prediction probability, and the class label value, and so on. The output schema is automatically set for models that are created by using model builder. It can also be set by using the IBM watsonx.ai Runtime Python client. Users can use the output schema to define a column that contains the string representation of the prediction. Set the modeling_role for the column to ‘decoded-target’. Read the [documentation for the watsonx.ai Runtime Python client library. Search for “OUTPUT_DATA_SCHEMA” to understand the output schema and the API to use is to store_model API that accepts the OUTPUT_DATA_SCHEMA as a parameter.

Why does Watson OpenScale need access to training data?

You must either provide Watson OpenScale access to training data that is stored in Db2 or IBM Cloud Object Storage, or you must run a Notebook to access the training data.

Watson OpenScale needs access to your training data for the following reasons:

• To generate contrastive explanations: To create explanations, access to statistics, such as median value, standard deviation, and distinct values from the training data is required.
• To display training data statistics: To populate the bias details page, Watson OpenScale must have training data from which to generate statistics.
• To build a drift detection model: The Drift monitor uses training data to create and calibrate drift detection.

In the Notebook-based approach, you are expected to upload the statistics and other information when you configure a deployment in Watson OpenScale. Watson OpenScale no longer has access to the training data outside of the Notebook, which is run in your environment. It has access only to the information uploaded during the configuration.

What does it mean if the fairness score is greater than 100 percent?

Depending on your fairness configuration, your fairness score can exceed 100 percent. It means that your monitored group is getting relatively more “fair” outcomes as compared to the reference group. Technically, it means that the model is unfair in the opposite direction.

How is model bias mitigated by using Watson OpenScale?

The debiasing capability in Watson OpenScale is enterprise grade. It is robust, scalable and can handle a wide variety of models. Debiasing in Watson OpenScale consists of a two-step process: Learning Phase: Learning customer model behavior to understand when it acts in a biased manner.

Application Phase: Identifying whether the customer’s model acts in a biased manner on a specific data point and, if needed, fixing the bias. For more information, see Debiasing options.

Is it possible to check for model bias on sensitive attributes, such as race and sex, even when the model is not trained on them?

Yes. Recently, Watson OpenScale delivered a ground-breaking feature called “Indirect Bias detection.” Use it to detect whether the model is exhibiting bias indirectly for sensitive attributes, even though the model is not trained on these attributes.

Is it possible to mitigate bias for regression-based models?

Yes. You can use Watson OpenScale to mitigate bias on regression-based models. No additional configuration is needed from you to use this feature. Bias mitigation for regression models is done out-of-box when the model exhibits bias.

What are the different methods of debiasing in Watson OpenScale?

You can use both Active Debiasing and Passive Debiasing for debiasing. For more information, see Debiasing options.

Configuring a model requires information about the location of the training data and the options are Cloud Object Storage and Db2. If the data is in Netezza, can Watson OpenScale use Netezza?

Use this Watson OpenScale Notebook to read the data from Netezza and generate the training statistics and also the drift detection model.

Why doesn't Watson OpenScale see the updates that were made to the model?

Watson OpenScale works on a deployment of a model, not on the model itself. You must create a new deployment and then configure this new deployment as a new subscription in Watson OpenScale. With this arrangement, you are able to compare the two versions of the model.

What are the various kinds of risks associated in using a machine learning model?

Multiple kinds of risks that are associated with machine learning models, such as any change in input data that is also known as Drift can cause the model to make inaccurate decisions, impacting business predictions. Training data can be cleaned to be free from bias but runtime data might induce biased behavior of model.

Traditional statistical models are simpler to interpret and explain, but unable to explain the outcome of the machine learning model can pose a serious threat to the usage of the model.

Must I keep monitoring the Watson OpenScale dashboard to make sure that my models behave as expected?

No, you can set up email alerts for your production model deployments in Watson OpenScale. You receive email alerts whenever a risk evaluation test fails, and then you can come and check the issues and address them.

In Watson OpenScale, what data is used for Quality metrics computation?

Quality metrics are calculated that use manually labeled feedback data and monitored deployment responses for this data.

In Watson OpenScale, can the threshold be set for a metric other than 'Area under ROC' during configuration?

No, currently, the threshold can be set only for the 'Area under ROC' metric.