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ESG Brief: Operationalizing AI: Time, Infrastructure Considerations, and Data Drift
ESG Brief
Jun 21, 2021
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.
ESG conducted a comprehensive online survey of IT professionals from private- and public-sector organizations in North America (United States and Canada) between August 3, 2021 and August 14, 2021. To qualify for this survey, respondents were require…