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Rayan Nait Mazi is a distinguished leader and expert in Artificial Intelligence and Data Strategy, currently serving as the Head of AI & Data Strategy at Artefact. With a strong focus on Generative AI, MLOps, and responsible AI, Rayan excels in translating complex technological advancements into tangible business value for global organizations. He is recognized for his strategic thinking, ability to lead high-performing data science teams, and his commitment to driving innovation by leveraging data-driven insights. Rayan is also a passionate educator and thought leader, frequently sharing his expertise through articles, conferences, and workshops, aiming to demystify AI and foster its ethical adoption across industries.
Rayan Nait Mazi's work history includes a series of influential roles in various companies. Here is a detailed list of his professional journey:
Successfully leads and scales the AI and Data Strategy practice at Artefact, defining visions, roadmaps, and overseeing the delivery of impactful AI solutions for international clients across various sectors including retail, CPG, luxury, and industrial goods.
Instrumental in researching, evangelizing, and implementing Generative AI solutions for businesses, helping them navigate the complexities and unlock the transformative potential of Large Language Models (LLMs) and other generative technologies.
Recognized as a prominent voice in the AI community, contributing insightful articles on Medium and other platforms, and speaking at industry events on topics like AI strategy, MLOps best practices, and the future of data-driven decision-making.
Spearheaded the development and deployment of innovative data products and AI-powered solutions that have demonstrably improved operational efficiency, customer engagement, and revenue growth for clients.
The Hong Kong University of Science and Technology - Year 2017
Ecole Centrale Paris - Year 2014
Lycée Henri IV - Year 2012
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Pruna AI is a technology company focused on making Large Language Models (LLMs) more efficient and accessible. They develop techniques to significantly reduce the size and computational requirements of LLMs, allowing them to run faster, on less powerful hardware (including consumer-grade devices), and at a lower cost. Their core offering involves model pruning and fine-tuning methodologies that maintain high performance while shrinking model parameters. This approach aims to democratize access to powerful AI, enabling more businesses and developers to leverage LLMs for innovative applications without prohibitive infrastructure costs.
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