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Although many enterprises are prioritizing investment in AI and machine learning, the business value is still obfuscated today because of challenges with moving AI models and proofs of concept out of the lab, into production, at scale. In order to truly unlock the value of AI, more focus must be given to model operationalization and integrating with existing infrastructure and applications. This report delves into AI engineering best practices for CTOs to get to the benefits and values of AI faster.

Gartner defines AI engineering as “a discipline focused on the governance and life cycle management of a wide range of operationalized AI and decision models. AI engineering methods enable better governance and consistency in reusing, retraining, rebuilding, interpreting and explaining AI models. These methods aim to provide an uninterrupted flow between the development, operationalization, and full maintenance of AI models.” AI engineering is built upon the following disciplines:

  • DataOps
  • DevOps
  • ModelOps
  • Responsible AI

Read this report for recommendations on establishing a healthy AI engineering practice to unlock the value of AI at scale.

ai engineering best practices

Gartner, A CTO’s Guide to Top Artificial Intelligence Engineering Practices, Arun Chandrasekaran, Farhan Choudhary, Erick Brethenoux, 29 October 2021.

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