Gartner predicts that by 2026, more than 30% of the increase in demand for APIs will come from artificial intelligence and tools using large language models. API sprawl is a hazardous form of technical debt. It has hard costs from hosting and scaling a fleet of APIs, real security risks and an increased operational burden from the sheer number of APIs that platform engineering teams need to manage. Training AI is expensive and can lead to the risk of leaking sensitive data or polluting a training set, so how can we get our LLMs, and AI-driven experiences the data they need safely and securely?
Learn use cases for implementing APIs in AI-driven experiences and how organizations can safely adopt AI on a spectrum from low to high trust.
View the Session Slides Here.
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