Overview of IBM watsonx as a Service

Tools for preparing data and working with models

Your watsonx.ai tools are in collaborative workspaces called projects.

You can use watsonx.ai tools to prepare data and work with models in the following ways:

• Prepare data: Refine and visualize your data files or data tables in remote data sources or generate synthetic tabular data.

• Build machine learning models: Automatically generate model candidates, create machine learning model training flows, or write model training code in Python or R.

• Work with foundation models: Experiment with generative AI prompts, tune foundation models for your use case, or write generative AI solution code in Python.

• Automate model lifecycles: Create repeatable and scheduled flows that automate notebook, Data Refinery, and machine learning pipelines.

For a list of tools, their levels of automation, and whether you can use them to prepare data or work with models, see Analyzing data and working with models. For generative AI tools, see Developing generative AI solutions.

Deployed foundation models

IBM watsonx.ai has a range of deployed large language models for generative AI. The deployed foundation models include open source models and IBM foundation models. You can also deploy your own custom foundation models. You can customize foundation model behavior by tuning a foundation model.

For a list of supported foundation models that are deployed in watsonx.ai, see Supported foundation models.

Usage resources

Depending on your service plans, you might have a set amount of usage resources per month or you might be billed for the resources that you consume.

When you run tools or host models on watsonx.ai, you consume the following types of resources:

• Compute usage: When you run jobs, notebooks, experiments that train or tune models, or deployments, your compute resource usage is calculated based on the rate for the runtime environment and its active duration. Compute resources include the appropriate hardware and software that are specific for the workload. Compute usage is measured in capacity unit hours. See Choosing compute resources for running tools in projects.

• Inferencing usage: When you run inferencing against foundation models, your inferencing usage is calculated as the sum of the tokens in the prompt input and output text multiplied by the rate for the foundation model. Tokens are basic units of text. Inferencing is measured in resource units. See Resource unit metering.

• Model hosting: When you deploy a custom foundation model, you are charged an hourly rate. Billing rates are according to model hardware configuration and apply for hosting and inferencing the model. Charges begin when the model is successfully deployed and continues until the model is deleted. See Hourly billing rates for custom foundation models.

• Text extraction: When you use text extraction to convert document files into an AI model-friendly JSON file format, you are charged per page. See Billing rates for document text extraction.

Tools for governing models

You can use watsonx.governance tools to govern models in the following ways:

• Monitor and evaluate models: You can monitor model output and explain model predictions. Your watsonx.governance model monitoring and evaluation tools are in projects and deployment spaces.

• Track and document AI use cases: You can view model lifecycle status, general model and deployment details, training information and metrics, and deployment metrics. Your watsonx.governance model tracking tools in AI use cases.

• Manage governance activity from the Governance console Optionally integrate with the watsonx Governance console from IBM OpenPages. Sync data from factsheets with the Governance console, and extend governance capabilities with workflows and other compliance tools.

Usage resources

When you run model evaluations and explanations with watsonx.governance, you consume resources. Depending on your service plans, you might have a set amount of usage resources per month or you might be billed for the resources that you consume. Your resource usage is calculated based on the number of model evaluations and explanations. Evaluations and explanations are measured in resource units.

Connectivity

You can create connections to remote data sources and import connected data. You can configure connections with personal or shared credentials. For a list of supported connectors, see Connectors.

You can share connections with others across the platform in the Platform assets catalog.

Administration

The following administration features provide security and flexibility:

• Software and hardware: Watsonx is fully managed by IBM on IBM Cloud. Software updates are automatic. Scaling of usage resources and storage is automatic.

• Security, compliance, and isolation: The data security, network security, security standards compliance, and isolation of watsonx are managed by IBM Cloud. Data is encrypted at rest and in motion. You can set up extra security and encryption options.

  Your work on watsonx, including your data and the models that you create, are private to your account. Your data and models will never be accessible or used by IBM or any other person or organization.

  Learn more about security and your options:

  • Security and privacy of foundation models
  • Data security
  • Security of watsonx

• Services provisioning: Watsonx.ai on the watsonx platform includes the watsonx.ai Studio, watsonx.ai Runtime, and IBM Cloud Object Storage services. Watsonx.governance on the watsonx platform includes the watsonx.governance service.

  You can add services, such as data source services, from the watsonx services catalog. See Creating and managing IBM Cloud services.

• User management: You add users and user groups and manage their account roles and permissions with IBM Cloud Identity and Access Management. You assign roles within each collaborative workspace across the platform. See Managing users and access.

Storage

A IBM Cloud Object Storage service instance is automatically provisioned for you to provide storage for the assets that you create or add to workspaces. Information that is stored in IBM Cloud Object Storage is encrypted and resilient. Each workspace has its own dedicated bucket. See Object storage for workspaces.

Workspaces

Watsonx is organized as a set of collaborative workspaces where you can work with your team or organization. Each workspace has a set of members with roles that provide permissions to perform actions.

Most users work with assets, which are items that are created or added to workspaces by users. Assets can represent data, models, or other types of code or information. Data assets contain metadata that represents data. Assets that you create in tools, such as models, run code to work with data. You can also create assets that contain information about other assets, such as model use cases that contain metadata, history, and reports about models. See Asset types and properties.

You can work in these types of workspaces in watsonx:

• Projects
• Deployment spaces
• Platform connections
• AI use cases

You can search for assets across all workspaces that you belong to.

Resource hub

Watsonx includes an integrated collection of samples that provides deployed foundation models, data assets, prompts, notebooks, and sample projects. Sample notebooks provide examples of data science and machine learning code. Sample projects contain sets of data, models, other assets, and detailed instructions on how to solve a particular business problem.

The following image shows what Resource hub looks like.

Watch this video to see a tour of the Resource hub.