Machine Learning Eco-system in a Modern Software Enterprise

Enterprises have crossed the corner stone moment of AI and ML adoption. We live in a world where it’s increasingly rare to find customers in any industry who aren’t impacted by AI, influenced by its potential, or actively seeking AI solutions to drive their success.

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Executive Summary

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 AI in our solution suites, it is crucial to prepare the organization to sustain and nurture long-term AI and ML initiatives effectively. Like any software innovation, AI and ML requires an ecosystem that defines best practices and establishes a centralized pillar.

At Sabre, we strive to innovate with AI 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 AI-ML ecosystem. This ecosystem empowers multiple teams to accelerate prototyping, streamline operations, and efficiently maintain large-scale AI-ML enterprise systems - all while upholding AI responsibility and ethical standards. This white paper presents a glimpse into the foundations of this ecosystem.

Tenants in an AI-ML ecosystem

To build an ecosystem, one would need to understand the tenants who would be coexisting within. ML today is not just restricted to Data Scientists or the core ML developers. For sustenance and continuity of such ecosystem, you have a plethora of teams that would be collaborating in the background.

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