Over the past years, transformer models had an enormous effect on machine learning approaches, and the reliability and comparability of the models.
With the increased capabilities of this technological advancement, industry requirements have shifted to address use cases such as intelligent knowledge search, automated image classification, and many more.
Join us on our journey from envisioning the perfect AI assistant to practical implementation, as we highlight the importance of critically evaluating the hype around these advancements. By doing so, we aim to distinguish between costly, flashy gadgets and sustainable value creation across various industries.
Anke Koke is a Data Scientist and ML Engineer at codecentric AG, specializing in AI project implementation, particularly in computer vision and MLOps. She focuses on generating tangible benefits for clients while streamlining existing workflows. With an innate curiosity, Anke continually explores and adopts cutting-edge tools that simplify her daily work. Committed to knowledge sharing, she enthusiastically embraces learning opportunities. Anke also contributes to the open-source project niceML as a co-developer.
Leonie Luttkus is a Business Solutions Consultant at codecentric AG, specializing in understanding and addressing customers' specific business challenges. Over the past two years, she has played a key role in the product management and development of codecentric's AI solution, SHERLOQ. Her work focuses on identifying digital, AI-based use cases to streamline knowledge, contract, and input management processes. With a background in Supply Chain Management, Leonie has a strong emphasis on process orientation. Before joining codecentric, she gained valuable experience working in project management teams at Deutsche Telekom and Amazon.
