IBM® watsonx™ is IBM’s strategic AI and data platform. This book focuses on watsonx.ai, one of the three main components of the platform. IBM watsonx.ai is a next-generation enterprise studio that you can use to train, validate (test), tune, and deploy both traditional ML and new gen AI capabilities, which are powered by FMs through an open and intuitive user interface (UI). This AI studio provides a range of FMs, training and tuning tools, and a cost-effective infrastructure that facilitates the entire data and AI lifecycle, from data preparation through model development, deployment, and monitoring. The studio also includes an FM library that provides IBM® curated and trained FMs. FMs use a large, curated set of enterprise data that is backed by a robust filtering and cleansing process, and with an auditable data lineage. These models are trained on language and other modalities, such as code, time-series data, tabular data, geospatial data, and IT events data.

Here are some examples of the model categories:

fm.code: Models that automatically generate code for developers through a natural-language interface to boost developer productivity and enable the automation of many IT tasks.

fm.NLP: A collection of large language models (LLMs) for specific or industry-specific domains that use curated data to help mitigate bias and quickly make domains customizable by using client data.

fm.geospatial: Models that are built on climate and remote sensing data to help organizations understand and plan for changes in natural disaster patterns, biodiversity, land use, and other geophysical processes that might impact their businesses

The watsonx.ai studio builds on Hugging Face open-source libraries, which offer thousands of Hugging Face open models and datasets. Users can leverage the power of IBM Granite LLMs, along with the latest Mistral, Llama, and other third-party LLMs. It is part of IBM's commitment to deliver an open ecosystem approach that enables users to leverage the best models and architecture for their unique business needs.

This IBM Redbooks publication provides a broad understanding of watsonx.ai concepts, its architecture, and the services that are available with the product. Also, several common use cases and scenarios are included that should help you better understand the capabilities of this product. Code samples of common scenarios are available at this GitHub repository:

https://github.com/IBM/watson-machine-learning-samples

For more examples, which include using Instructlab and AI agents, see this GitHub repository:

https://github.com/IBM/watsonx-ai-platform-demos

This publication is for watsonx customers who seek best practices and real-world examples of how to best implement their solutions while optimizing the value of their existing and future technology, AI, data, and skills investments.

Here are the other books in the trilogy:

Simplify Your AI Journey: Ensuring Trustworthy AI with IBM watsonx.governance, SG24-8573

Simplify Your AI Journey: Unleashing the Power of AI with IBM watsonx.data, SG24-8570

Chapter 1. Competing with artificial intelligence

Chapter 2. Introducing IBM watsonx.ai

Chapter 3. Tools for diverse data science teams

Chapter 4. Building and using artificial intelligence models

Chapter 5. Advanced capabilities of watsonx.ai

Chapter 6. Artificial intelligence agents

Chapter 7. Use cases