Insights / Research Brief / Operationalizing AI: Time, Infrastructure Considerations, and Data Drift
June 22, 2021

Operationalizing AI: Time, Infrastructure Considerations, and Data Drift

Mike Leone
Principal Analyst, Data Analytics & AI

Market Topics

Data Analytics & Artificial Intelligence

Though the cyclical AI lifecycle is riddled with complexity, the last mile of AI is proving to be the greatest challenge for organizations in their quest to leverage AI. Between diverse and distributed application environments, the rate at which growing data sets change and create data drift, and the dynamic needs of the business, several contributing factors lead to organizations suffering from AI deployment challenges. Both new and mature businesses leveraging AI continue to prioritize opportunities to simplify the last mile of AI—deploying AI into production—with a goal of reducing the amount of time it takes to get from trained model to production. This has paved the way for the emergence of technology to better enable businesses to deploy, track, manage, and iterate on a growing number of ML models in production environments.

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