Enterprises have crossed the corner stone moment of Al and ML adoption. We live in a world where it’s increasingly rare to find customers in any industry who aren’t impacted by Al, influenced by its potential, or actively seeking Al solutions to drive their success. The use cases are diverse, ranging from traditional supervised machine learning, which drives business evolution, to cutting-edge technologies like large language models (LLMs) and retrieval-augmented generation systems (RAGS), enhancing customer experiences and simplifying interactions with backend business processes.
While we have been enthusiastically finding innovative ways to insert Al in our solution suites, it is crucial to prepare the organization to sustain and nurture long-term Al and ML initiatives effectively. Like any software innovation, Al and ML requires an ecosystem that defines best practices and establishes a centralized pillar.
At Sabre, we strive to innovate with Al and ML, creating solutions that deliver value across the spectrum – from empowering airline’s dynamic retailing strategies to enhancing the experience for airline passengers. Through the process of exploring and implementing ideas, we recognized the critical need to establish a robust Al-ML ecosystem. This ecosystem empowers multiple teams to accelerate prototyping, streamline operations, and efficiently maintain large-scale Al-ML enterprise systems – all while upholding Al responsibility and ethical standards. This white paper presents a glimpse into the foundations of this ecosystem.
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This technical article authored by Tushar Bisht was published on BIS Infotech.