Overview of IBM watsonx.ai and IBM watsonx.governance software
IBM watsonx is a set of services on IBM Software Hub that provides a secure and collaborative environment where you can access your organization's trusted data, automate AI processes, and deliver AI in your applications. The IBM watsonx.ai component provides a studio of integrated tools for builing generative AI solutions that are powered by foundation models and for building machine learning models. The IBM watsonx.governance component provides end-to-end monitoring for machine learning and generative AI models to accelerate responsible, transparent, and explainable AI workflows.
AI engineers, data scientists, and AI risk and compliance officers can accomplish the following goals with watsonx.ai and watsonx.governance:
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Build machine learning models: Build models by using open source frameworks and code-based, automated, or visual data science tools.
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Develop with foundation models: Generate prompts to generate, classify, summarize, or extract content from your input text. Choose from IBM models or open source models from Hugging Face. Tune foundation models to customize your prompt output.
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Manage the AI lifecycle: Manage and automate the full AI model lifecycle with all the integrated tools and runtimes to train, validate, and deploy AI models.
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Govern AI: Track the detailed history of AI models and evaluate model output to help ensure compliance.
Data engineers can collect, store, query, and analyze enterprise data in a lakehouse architecture with IBM watsonx.data. See IBM watsonx.data 2.1.x documentation. However, IBM watsonx.data has a separate experience from watsonx.ai and watsonx.governance.
The watsonx experience is comprised of a set of integrated services on IBM Software Hub. IBM Software Hub is a modular platform for running various solutions and services on a multi-node Red Hat OpenShift Container platform. The Red Hat OpenShift AI platform layer is for hosting and inferencing foundation models.
The following graphic shows the architecture of the watsonx experience. The common core services provide an integrated user experience.
If your IBM Software Hub platform contains multiple solutions, you might have access to other experiences, such as watsonx. You can switch from Cloud Pak for Data to any other experiences that you have permission to access. See Switching between experiences.
The watsonx experience does not include any data governance or data integration functionality. To govern or integrate data, switch to the Cloud Pak for Data experience. See the Cloud Pak for Data documentation.
Watsonx.ai
Watsonx.ai and its associated services include tools for working with data and models, the foundation models for generative AI, and the hardware and software resources for computing and inferencing. Which tools, foundation models, and hardware and software resources that you have access to depend on which services are installed on your system.
You can optionally install watsonx.ai lightweight engine, which is a slimmer deployment that offers functions you need only at run time, such as hosting and inferencing foundation models. For more information, see Choosing an installation mode in the IBM Software Hub documentation.
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:
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Prepare data: Refine and visualize your data files or data tables in remote data sources or generate synthetic tabular data.
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Build machine learning models: Automatically generate model candidates, create machine learning model training flows, or write model training code in Python or R.
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Work with foundation models: Experiment with generative AI prompts, tune foundation models for your use case, or write generative AI solution code in Python.
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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 Data science solutions. For generative AI tools, see Developing generative AI solutions.
Deployed foundation models
IBM watsonx.ai has a range of large language models for generative AI that your administrator can deploy in your cluster. 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 included foundation models that your administrator can deploy for watsonx.ai, see Supported foundation models.
Watsonx.governance
Watsonx.governance includes tools for governing, evaluating, and explaining models.
Tools for governing AI
You can use watsonx.governance tools to govern AI in the following ways:
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Monitor and evaluate AI assets: You can monitor machine learning model output and explain model predictions. You can evaluate and compare generative AI prompts. Your watsonx.governance model monitoring and evaluation tools are in projects and deployment spaces.
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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.
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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.
For a list of governance tools, see Governing assets with watsonx.governance.
Common core functionality
Watsonx services require integrated features and interfaces. These features are provided by the IBM Software Hub common core services. The common core services provide data source connections, workspaces such as projects and deployment spaces, job management, notifications, and search.
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
Your cluster administrators manage IBM watsonx through the IBM Software Hub. Administrators can perform the following types of tasks:
- Installing, upgrading, or migrating the software
- Backing up or restoring the software
- Monitoring the platform
- Securing the environment
- Auditing events
- Forwarding alerts, notifications, and announcements
- Setting up services
- Managing resources
- Managing users
See Administering IBM Software Hub in the IBM Software Hub documentation.
Storage
IBM watsonx and Cloud Pak for Data require a persistent storage solution that is accessible to your Red Hat OpenShift cluster. All the assets that you create with watsonx.ai and watsonx.governance are stored in that persistent storage solution.
See Storage requirements in the IBM Software Hub documentation.
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
- Inventories
You can search for assets across all workspaces that you belong to.
Projects
Projects are where your data science and model builder teams work with data to create assets, such as, saved prompts, notebooks, models, or pipelines.
If you also have the Cloud Pak for Data experience, your projects appear in both experiences. However, you can view and run only those assets that are valid in the current experience. For example, in the watsonx experience, you can't enrich the metadata of a data asset.
The following image shows what the Overview page of a project might look like.
Deployment spaces
Deployment spaces are where your ModelOps team deploys models and other deployable assets to production and then tests and manages deployments in production. After you build models and deployable assets in projects, you promote them to deployment spaces. See Deployment spaces.
The following image shows what the Overview page of a deployment space might look like.
Platform connections
Platform connections is a view of the Platform assets catalog that lists connection assets. You can access platform connections in any project or deployment space.
The following image shows what the Connections page of the Platform connections might look like.
Inventories
Inventories store AI use cases. Use cases track the details for AI assets in factsheets. You can also view all AI use cases in all inventories.
The following image shows what the AI use cases page might look like.
