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Businesses are under pressure to show return on investment (ROI) from AI use cases, whether predictive machine learning (ML) or generative AI. Only 54% of ML prototypes make it to production, and only 5% of generative AI use cases make it to production. Using SageMaker, you can build, train and deploy ML models.
Specialist DataEngineering at Merck, and Prabakaran Mathaiyan, Sr. MLEngineer at Tiger Analytics. The large machine learning (ML) model development lifecycle requires a scalable model release process similar to that of software development. This post is co-written with Jayadeep Pabbisetty, Sr.
With organizations increasingly investing in machine learning (ML), ML adoption has become an integral part of business transformation strategies. However, implementing ML into production comes with various considerations, notably being able to navigate the world of AI safely, strategically, and responsibly.
It requires checking many systems and teams, many of which might be failing, because theyre interdependent. Developers need to reason about the systemarchitecture, form hypotheses, and follow the chain of components until they have located the one that is the culprit.
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