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Streamline Sales Processes with Enterprise CPQ

Cincom

Integration with Existing Systems: APIs facilitate data sharing between CPQ and other core platforms like CRM, ERP, accounting, e-commerce, and more. Configurable rules encode compliance requirements and best practices to guide users and skip unnecessary steps.

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Configure an AWS DeepRacer environment for training and log analysis using the AWS CDK

AWS Machine Learning

With the help of the AWS CDK, we can version control our provisioned resources and have a highly transportable environment that complies with enterprise-level best practices. Make sure you have the credentials and permissions to deploy the AWS CDK stack into your account.

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

AWS Machine Learning

However, putting an ML model into production at scale is challenging and requires a set of best practices. Integrations with CI/CD workflows and data versioning promote MLOps best practices such as governance and monitoring for iterative development and data versioning.

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Enhance call center efficiency using batch inference for transcript summarization with Amazon Bedrock

AWS Machine Learning

We also explore best practices for optimizing your batch inference workflows on Amazon Bedrock, helping you maximize the value of your data across different use cases and industries. Prerequisites To use the batch inference feature, make sure you have satisfied the following requirements: An active AWS account.

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Bring SageMaker Autopilot into your MLOps processes using a custom SageMaker Project

AWS Machine Learning

We demonstrate how to use CI/CD the low-code/no-code tools code to integrate it into your MLOps environment, while adhering with MLOps best practices. To learn more about SageMaker Projects and creating custom project templates aligned with best practices, refer to Build Custom SageMaker Project Templates – Best Practices.