In recent times, GenAI has sparked fervent excitement, sometimes touted as the panacea for all natural language processing (NLP) tasks. This presentation explores a practical text classification scenario at Malt, highlighting the practical hurdles encountered when employing GenAI (latency, environmental impact, and budgetary constraints). To overcome these obstacles, a smaller, dedicated model emerged as a viable solution. We'll delve into the construction and optimization (quantization, graph optimization) of this multilingual model. Finally we’ll see how GenAI's unparalleled zero-shot capabilities enables its continuous adaptation.
Marc Palyart is a Staff Data Scientist at Malt, the freelancer marketplace, where he leads the search and matching team. With over a decade of data-wizardry under his belt, he's ventured into the depths of academia and scaled the heights of industry where he's had the pleasure of collaborating with some truly remarkable people.