A well-considered approach to AI can help you scale and operationalise more quickly and more effectively. The IBM approach is to combine generative AI with traditional machine learning techniques. Generative AI provides scalability through foundation models that are trained on unlabelled data. Traditional machine learning techniques offer fine-tuning and customisation, using labelled data for improved accuracy.
Meet IBM Watsonx.ai™, an enterprise-ready AI studio for AI builders. Build with our new studio for foundation models, generative AI and machine learning. With watsonx.ai users can leverage foundation models in a variety of ways including accessing open source models, IBM proprietary models, domain specific models, and bring your own models.

Build AI applications in a fraction of the time, with a fraction of the data.

Guide models to meet your needs using tools for building and refining performant prompts in the Prompt Lab.

Tune models with your enterprise data in just 5 clicks; your data and models remain yours, private and secured in the Tuning Studio.

Generative AI

  • Build, train and deploy machine learning models with an MLOps collaborative studio.
  • Construct solution engines for mathematical and constraint programming to address decision-optimization use cases.
  • Automate data preparation, model development, feature engineering and hyper-parameter optimization.
  • Create data preparation and predictive machine learning pipelines with visual modeling.
  • Get complete API and SDK for application integration.

Machine learning

  • Manage tasks such as content generation and extraction with foundation model libraries and use IBM-selected open-source models from Hugging Face.
  • Use prompts to summarize documents, create content for marketing campaigns, extract information from unstructured text, and more.
  • Use APIs and SDKs to integrate generative AI with applications.

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