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WeBuild-AI are making the case that bigger isn't always better. Adel’s talk breaks down how small language models, through distillation, fine tuning and low rank adaptation, can deliver the same results as larger models, at a fraction of the cost.

Large language models have dominated the AI conversation, but a quieter revolution is underway. In this talk, we explores the rise of small language models and why they matter now more than ever, from reduced infrastructure costs to data sovereignty and the ability to run AI on-premise or at the edge. We'll dive into the techniques that make small models punch above their weight, including distillation, quantisation, fine-tuning and low-rank adaptation. Whether you're navigating regulatory constraints, working with limited compute, or simply looking for a more practical path to production AI, this session will give you the tools to think smaller and smarter.

About the speaker:

Adel is a Principal Consultant at WeBuild-AI.

Adel has a decade of experience in building and scaling AI and ML solutions at different industries and different company sizes, ranging from series A to unicorns and more recently he was a Sr. AI Engineering Manager at Procter & Gamble.

He has also published several research papers in the areas of AI Explainability and NLP and is an advocate of safe and explainable AI.