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Amazon Bedrock Custom Model Import now generally available

AWS Machine Learning

This feature empowers customers to import and use their customized models alongside existing foundation models (FMs) through a single, unified API. This broad compatibility allows customers to work with models that best suit their specific needs and use cases, allowing for greater flexibility and choice in model selection. 2, 3, 3.1,

APIs 139
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Automate cloud security vulnerability assessment and alerting using Amazon Bedrock

AWS Machine Learning

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.

APIs 73
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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. Batch job submission – Initiate and manage batch inference jobs through the Amazon Bedrock console or API.

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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

The following diagram illustrates the solution architecture. 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.

Scripts 85
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Reinvent personalization with generative AI on Amazon Bedrock using task decomposition for agentic workflows

AWS Machine Learning

In parallel, OneCompany maintains a market research repository gathered by their researchers, offers industry-specific services outlined in documents, and has compiled approved customer testimonials. UX/UI designers have established best practices and design systems applicable to all of their websites.

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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.