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Get the most from your AI investment by operationalizing analytics

Published by SAS

Operationalizing analytics is essential for maximizing the value of AI investments by integrating models into production systems efficiently. The document emphasizes the importance of ModelOps, which bridges the gap between data science and IT, ensuring that analytical models are continuously monitored, retrained, and updated to maintain peak performance. Key steps include deploying models, embedding them into decision-making processes, and using real-time monitoring to track performance. By operationalizing analytics, organizations can align their analytics investments with business objectives, driving faster insights and ensuring that models deliver ongoing value.

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