Insights to keep AI applications reliable

AI has become a significant investment for companies, and maintaining the reliability of AI applications requires both traditional and innovative approaches. Despite AI applications running on existing infrastructure, they introduce complexities such as new traffic patterns and dependencies, necessitating adjustments in operational strategies. Key challenges include ensuring both the availability of AI systems and the accuracy of their responses, which requires collaboration between DevOps and AI engineers. As AI continues to evolve, organizations must balance enabling new technologies while setting appropriate guardrails and testing processes to minimize customer impact. Engineering teams play a crucial role in maintaining AI reliability by defining metrics, conducting resilience testing, and integrating AI specialists into incident response plans. The ongoing development of best practices, such as GPU testing and specific SLOs, underscores the need for continuous learning and adaptation in the field of AI operations.

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