IBM watsonx.ai

Watsonx.ai is an enterprise-ready next-generation AI studio for machine learning and generative AI, powered by foundation models. With the watsonx.ai studio, AI builders — including data scientists, application developers and business analysts can build, train, validate, tune and deploy traditional machine learning and new generative AI capabilities. Watsonx.ai is designed to support collaboration and scalability in AI application development and can be deployed across hybrid environments.

• Foundation Models: Clients have access to IBM-developed foundation models of different sizes and architectures. These models start with the Slate family for non-generative AI tasks and the Granite series of models that use a decoder architecture to support a variety of enterprise level generative AI tasks. The collection also includes a curated selection of open source foundation models from Hugging Face, as well as third-party models for both language and code generation.

• Prompt Lab: AI builders can work with foundation models and build prompts using prompt engineering. Within the Prompt Lab, users can experiment with zero-shot, one-shot, or few-shot prompting to support a range of Natural Language Processing (NLP) type tasks including question answering, content generation, summarization, text classification, and extraction. It also includes configurable model parameters that allow optimal experimentation with foundation model output, and optional “AI guardrails” to help filter out hateful, abusive or profane content from prompts and model output.

• Synthetic Data: AI builders and data scientists can generate synthetic tabular data by importing data from a database, uploading a file, or creating a custom data schema. The statistics-based model can be used to improve the predictive accuracy of AI training models via edge cases and larger sample sizes, as well as the realism of client demos and employee training materials. The Synthetic Data Generator is available as part of the data science and MLOps toolset.

• Data Science and MLOps toolset: A comprehensive set of tools, both programmatic and visual, that cover the full spectrum of AI application development and deployment, including: data preparation, synthetic data generation, python/R notebooks, open source libraries, API’s/SDK’s, visual tools to build data pipelines and flows, auto generated predictive models, federated learning, and an interface for prescriptive analytics used for decision optimization.