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Building on the foundation covered in Part 1, this guide takes a deeper look at how teams can operationalize AI in their R&D environments. We explore how to structure data for model training, evaluate model outputs, and integrate AI recommendations into your existing experimental workflows.
Key topics include feedback loops between AI predictions and lab results, iterative model improvement, and best practices for model governance in regulated industries. If your team is ready to move from experimentation with AI to full deployment, this resource is for you.
