Overview of the watsonx experience
The watsonx experience is a set of services on IBM Software Hub that provides a secure and collaborative environment where you can build, evaluate, and deliver AI in your applications. The IBM watsonx.ai services provide a studio of integrated tools for building generative AI and machine learning solutions. The IBM watsonx.governance service provides end-to-end evaluation and monitoring for AI solutions to help ensure 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:
- Build generative AI solutions
- Build solutions that include prompts, agents, retreival-augmented generation (RAG) patterns, and other capabilities of foundation models. Choose from IBM models, third-party models, open source models, or import custom foundation models. Tune foundation models to customize your prompt output.
- Build machine learning solutions
- Build models with open source frameworks and code-based, automated, or visual data science tools. Manage and automate the model lifecycle with integrated tools and runtimes to train, validate, and deploy machine learning models.
- Govern AI
- Track the detailed history of AI models, assess risks, and evaluate model output to help ensure compliance.
Platform architecture
The watsonx is part of the IBM watsonx platform. The IBM Software Hub platform has multiple integrated experiences that share services and workspaces. The experiences that you can access depend on which services are installed on your IBM Software Hub cluster. An experience provides focused access to the tools for specific tasks.
The IBM Software Hub platform includes these integrated experiences:
- watsonx, which contains the Watson Studio, Watson Machine Learning, and IBM watsonx.governance services for building and governing AI solutions.
- Data Fabric, which contains the watsonx.data intelligence service for preparing and sharing high-quality, trusted data products.
- watsonx.data, which contains the watsonx.data Premium, watsonx.data intelligence, watsonx.ai, and related services for preparing unstructured data for AI.
- Cloud Pak for Data, which contains many of the same services as the other experiences but without generative AI or unstructured data processing capabilities.
- Data Product Hub, which contains the Data Product Hub service for sharing data products without the rest of the Data Fabric capabilities.
Projects are shared between the experiences so that users with different tasks can work together. You can switch between experiences that you have permission to access to use different tools. Users who are collaborating in the same project can work in different experiences. For example, suppose a data engineer and an AI engineer are collaborators in the same project. The data engineer, who is working in the Data Fabric experience, prepares a data asset. The AI engineer, who is working in the watsonx experience, uses the data asset to train a model. See Switching between experiences.
The following illustration shows the architecture of the integrated experiences on the IBM Software Hub platform, the services and capabilities for each experience, and the shared functionality that provides an integrated user experience.
Watsonx.ai
Watsonx.ai and its associated services include APIs and tools for building AI solutions, 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.
The watsonx experience on IBM Software Hub runs on a multi-node Red Hat OpenShift Container platform. A Red Hat OpenShift AI platform layer is required for hosting and inferencing foundation models.
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.
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
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- Refine and visualize your data files or data tables in remote data sources with Data Refinery.
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- Generate synthetic structured data for training machine learning models with Synthetic Data Generator.
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- Generate synthetic unstructured data for tuning foundation models or testing gen AI solutions with the synthetic data generation API.
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- Vectorize your unstructured data for RAG patterns with vector indexes.
- See Preparing data.
- Build generative AI solutions
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- Write generative AI solution code with Python SDKs, REST APIs, or Node.js.
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- Experiment with generative AI prompts in the Prompt Lab.
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- Automate RAG patterns with AutoAI.
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- Tune foundation models for your use case with Tuning Studio.
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- Build AI agents with Agent Lab.
- See Developing generative AI solutions.
- Build machine learning models
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- Automatically generate predictive model candidates with AutoAI.
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- Create machine learning model training flows with SPSS Modeler.
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- Write Jupyter notebooks to train models in Python or R.
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- Solve optimization problems with Decision Optimization.
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- Automate the machine learning lifecycle with Orchestration Pipelines.
- See Data science 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 APIs and tools for governing, evaluating, and explaining models.
Ways to govern AI
You can use watsonx.governance APIs and tools to govern AI in the following ways:
- Monitor and evaluate AI assets
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- Monitor machine learning model output and explain model predictions.
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- 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
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- Sync data from factsheets with the Governance console.
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- 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.
Shared functionality
Watsonx includes the following functionality that is shared between services and experiences for secure and scalable collaboratation:
- Connectivity
- Administration
- Storage
- Workspaces
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 the IBM watsonx experience through the IBM Software Hub platform. 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
The IBM Software Hub platform requires 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
The watsonx experience 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.
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.
Your projects are shared across the integrated 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 Platform assets catalog is shared across integrated experiences.
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.
