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Driving advanced analytics outcomes at scale using Amazon SageMaker powered PwC’s Machine Learning Ops Accelerator

AWS Machine Learning

Overall, ML use cases require a readily available integrated solution to industrialize and streamline the process that takes an ML model from development to production deployment at scale using MLOps. Data and model management provide a central capability that governs ML artifacts throughout their lifecycle.

Analytics 104
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Accenture creates a regulatory document authoring solution using AWS generative AI services

AWS Machine Learning

Companies face complex regulations and extensive approval requirements from governing bodies like the US Food and Drug Administration (FDA). Users then review and edit the documents, where necessary, and submit the same to the central governing bodies. Richa Gupta is a Technology Architect at Accenture, leading various AI projects.

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Calabrio Charts Record Year-on-Year UK Growth as Demand for Cloud Technology Soars During Lockdown

CSM Magazine

Digital transformation acceleration drives cloud contact centre adoption of Calabrio workforce engagement management technology. These organisations require an innovative yet reliable solution to help them manage unprecedented levels in demand.”. Before joining Calabrio, Niall spent 6 years with Qlik as Industry Solutions Director.

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Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning

MLOps – Model monitoring and ongoing governance wasn’t tightly integrated and automated with the ML models. Reusability – Without reusable MLOps frameworks, each model must be developed and governed separately, which adds to the overall effort and delays model operationalization.