Overview of IBM watsonx as a Service

Ways to prepare data and build AI solutions

For most tasks, you can choose between writing code and working with tools in the UI. Your watsonx.ai tools are in collaborative workspaces called projects.

You can prepare data and build AI solutions in the following ways:

Prepare data for AI

• Refine and visualize your data files or data tables in remote data sources with Data Refinery.

• Generate synthetic structured data for training machine learning models with Synthetic Data Generator.

• Generate synthetic unstructured data for tuning foundation models or testing gen AI solutions with the synthetic data generation API.

• Vectorize your unstructured data for RAG patterns with vector indexes.

See Preparing data.

Build generative AI solutions

• Write generative AI solution code with Python SDKs, REST APIs, or Node.js.

• Experiment with generative AI prompts in the Prompt Lab.

• Automate RAG patterns with AutoAI.

• Tune foundation models for your use case with Tuning Studio.

• Build AI agents with Agent Lab.

See Developing generative AI solutions.

Build machine learning models

• Automatically generate predictive model candidates wihh AutoAI.

• Create machine learning model training flows with SPSS Modeler.

• Write Jupyter notebooks to train models in Python or R.

• Solve optimization problems with Decision Optimization.

• Automate the machine learning lifecycle with Orchestration Pipelines.

See Data science 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 or deploy an on-demand model. 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.

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.

Model hosting

When you deploy a custom foundation model or a deploy on demand 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.

Text extraction

When you use text extraction to convert document files into an AI model-friendly JSON file format, you are charged per page.

Learn more about usage and billing:

• Billing details for watsonx.ai Studio tools
• Billing details for generative AI assets
• Billing details for machine learning assets

Ways to govern AI

You can use watsonx.governance APIs and tools to govern AI in the following ways:

Monitor and evaluate AI assets

• Monitor machine learning model output and explain model predictions.

• Evaluate and compare generative AI prompts.

Your watsonx.governance model monitoring and evaluation tools are in projects and deployment spaces.

See Evaluating AI assets.

Track and document AI use cases

View model lifecycle status, general model and deployment details, training information and metrics, and deployment metrics with AI use cases.

See Governing assets in AI use cases.

Manage governance activity

• Sync data from factsheets with the Governance console.

• Extend governance capabilities with workflows and other compliance tools with the Governance console.

You must integrate with the watsonx Governance console from IBM OpenPages.

See Managing risk and compliance with Governance console.

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.

See IBM watsonx.governance plan options.

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

You can add services from the watsonx services catalog. See Creating and managing IBM Cloud services.

You access some services in other experiences. For example, if you add the watsonx.data intelligence service, you must switch to the Data Fabric experience to use it.

You can add data source services and create connections to them from watsonx experiences, but you manage data source services from the IBM Cloud console.

User management

You add users and user groups and manage their IBM Cloud 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

When you sign up for a watsonx experience, a IBM Cloud Object Storage service instance is automatically provisioned to provide storage for the assets that you create or add to workspaces. The IBM Cloud Object Storage service instance is shared across experiences. 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 the watsonx experience:

• Projects
• Deployment spaces
• Platform connections
• Inventories

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.