Real ROI: Navigating Challenges and Technical Debt in LLMs Production Deployment скачать в хорошем качестве

Real ROI: Navigating Challenges and Technical Debt in LLMs Production Deployment

Generative AI have become essential in advancing AI, enabling remarkable capabilities in natural language processing and understanding. However, the efficient deployment of LLMs in production environments reveals a landscape of challenges and technical debt Ethically, LLMs face issues such as bias amplification, where they might perpetuate existing stereotypes in their outputs. Misinformation is another concern, with the potential misuse of LLMs to create convincing yet false narratives. Privacy risks emerge from LLMs possibly memorizing and revealing personal data. Moreover, societal challenges include the impact on employment, as LLMs could automate tasks but also lead to job displacement. These challenges highlight the need for careful management and ethical considerations in the deployment of LLMs In this talk, Ahmed Menshawy Vice President of AI Engineering, Mastercard will highlight the key challenges and technical debt associated with LLMs' deployment, which demands customization and sophisticated engineering solutions not readily available in broad-use machine learning libraries or inference engines