Whitepapers

Understand the importance of pilot projects in making Generative AI practical; address issues of scalability and mobility; ensure secure transitions between different Generative AI models and explore the intricacies of Large Language Models (LLMs) and Small Language Model.

Leveraging the Most Disruptive Tool Since the Internet for Commercial, Operational & Competitive Success.

With corporations all at various stages of Generative AI adoption and still much transformation ahead, setting a clear, long-term path, business case, motivation and measurement is essential.

The expectations of generative AI’s potential are high, but it is important for businesses to be realistic about current limitations and the pace of technological change

Lukasz Szpruch, Programme Director for Finance and Economics at The Alan Turing Institute on the role of synthetic data in advancing generative AI development

Together with SAP, the AI, Data & Analytics Network has surveyed hundreds of data professionals to understand the spends and the trends of the past year.

Download our latest report to discover how companies are responding to data talent shortages.

23-page, in-depth report on AI, data and analytics trends.

Red Hat's secrets to transforming data into a valuable business asset and a competitive advantage

By bringing together automation, analytics and upskilling into one unified platform, analytics automation not only optimizes data processes, it democratizes them

5 myths about AI-powered document processing and how to combat them

To turn data into a critical business asset, organizations are adopting artificial intelligence (AI), machine learning (ML), and deep learning (DL). This e-book highlights top considerations for building a production-ready AI/ML environment to support business goals.