Red Hat AI: Modular building blocks for scalable, repeatable model customization

Red Hat AI: Modular building blocks for scalable, repeatable model customization Docling: Document intelligence SDG hub: Collection of high quality synthetic data pipelines Conclusion The adaptable enterprise: Why AI readiness is disruption readiness About the authors Aditi Saluja Jehlum Vitasta Pandit Ana Biazetti More like this Blog post Blog post Original podcast Original podcast Keep exploring Browse by channel Automation Artificial intelligence Open hybrid cloud Security Edge computing Infrastructure Applications Virtualization Share Taking generative AI (gen AI) from experimentation to enterprise deployment is never one-size-fits-all. Healthcare, finance, manufacturing, and retail each have their own vocabularies, data quirks, and compliance challenges. These complexities can’t be addressed with generic workflows because real, business-critical deployments demand depth, control, and precision. Red Hat AI offers a model customization experience that builds on the success of InstructLab , evolving it into a modular architecture powered by Python libraries created by Red Hat. This approach preserves InstructLab’s core strengths—its open, extensible pipeline for fine-tuning and instruction-following—while enabling greater flexibility and scalability for enterprise environments. With this foundation, AI experts and ML practitioners can adapt methods, orchestrate pipelines, and scale model training without losing the agility to evolve as techniques improve. The result is a more sophisticated path to enterprise-grade model customization—one that delivers adaptability and control without sacrificing speed. At the heart of this evolution are 3 core components that enable experts to fine-tune models and integrate them effectively with retrieval-augmented generation (RAG). Docling for data processing SDG hub for synthetic data generation (SDG) Training hub for fine-tuning and continual learning Since these components are modular, you can use them independently or connect them end-to-end. We will also provide more supported notebooks and AI/data science pipelines that teach you the technology as well as how to adapt it to your use case, your data, your model, and run it reliably by combining data processing, synthetic data generation, and fine-tuning techniques. Docling is our supported solution for data processing. It’s the number one open source repository for document intelligence.