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.

Use cases

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.

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

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.

See Developing generative AI solutions.

Build machine learning models

Automatically generate predictive model candidates with 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 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

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.

Share governed AI agents and tools: Register, manage, evaluate, and reuse AI-powered agents and tools in the governed agentic catalog.; See Governed agentic catalog.

Manage and share AI guardrail policies: Create, manage, and reuse guardrail policies in an inventory.; See Configuring guardrails.