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Boost team productivity with Amazon Q Business Insights

AWS Machine Learning - AI

Amazon Q Business Insights provides administrators with details about the utilization and effectiveness of their AI-powered applications. By monitoring utilization metrics, organizations can quantify the actual productivity gains achieved with Amazon Q Business.

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Best practices and lessons for fine-tuning Anthropic’s Claude 3 Haiku on Amazon Bedrock

AWS Machine Learning - AI

Fine-tuning is a powerful approach in natural language processing (NLP) and generative AI , allowing businesses to tailor pre-trained large language models (LLMs) for specific tasks. By fine-tuning, the LLM can adapt its knowledge base to specific data and tasks, resulting in enhanced task-specific capabilities. Sonnet vs.

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Cepsa Química improves the efficiency and accuracy of product stewardship using Amazon Bedrock

AWS Machine Learning - AI

An approach to product stewardship with generative AI Large language models (LLMs) are trained with vast amounts of information crawled from the internet, capturing considerable knowledge from multiple domains. However, their knowledge is static and tied to the data used during the pre-training phase.

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Using Interface Classification in Kentik Detect

Kentik

For a closer look at how this is achieved, check out the Interface Classification article in our Knowledge Base. Interface classifications may be accessed here in two forms: Group-by dimensions (sidebar Query pane): Set the combination of fields that define a set of traffic that can be counted (by metric) and ranked.

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The Best 39 Development Tools

Codegiant

It is a knowledge base or wiki that stores and organizes all of the different projects’ information assets. Presents real-time dashboards with mix-and-match events and metrics from linked services, containers, hosts, and apps. User Review “ Great for working within a design team but has some flaws. Provides alert notifications.

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The Best 40 Development Tools

Codegiant

It is a knowledge base or wiki that stores and organizes all of the different projects’ information assets. Presents real-time dashboards with mix-and-match events and metrics from linked services, containers, hosts, and apps. It allows users to create effective strategies to improve their services, apps, and tools.

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A guide to Amazon Bedrock Model Distillation (preview)

AWS Machine Learning - AI

Under Distillation output metrics data , for S3 location , enter the S3 path for the bucket where you want the training output metrics of the distilled model to be stored. Optionally, expand the VPC settings section to specify a VPC that defines the virtual networking environment for this distillation job.