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Orchestrate an intelligent document processing workflow using tools in Amazon Bedrock

AWS Machine Learning

The solution uses the FMs tool use capabilities, accessed through the Amazon Bedrock Converse API. This enables the FMs to not just process text, but to actively engage with various external tools and APIs to perform complex document analysis tasks. For more details on how tool use works, refer to The complete tool use workflow.

APIs 93
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Achieve operational excellence with well-architected generative AI solutions using Amazon Bedrock

AWS Machine Learning

It’s a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like Anthropic, Cohere, Meta, Mistral 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.

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Evaluate large language models for your machine translation tasks on AWS

AWS Machine Learning

The translation playground could be adapted into a scalable serverless solution as represented by the following diagram using AWS Lambda , Amazon Simple Storage Service (Amazon S3), and Amazon API Gateway. Also note the completion metrics on the left pane, displaying latency, input/output tokens, and quality scores.

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Security best practices to consider while fine-tuning models in Amazon Bedrock

AWS Machine Learning

Analyze results through metrics and evaluation. Under Output data , for S3 location , enter the S3 path for the bucket storing fine-tuning metrics. Model customization in Amazon Bedrock involves the following actions: Create training and validation datasets. Set up IAM permissions for data access. Configure a KMS key and VPC.

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Building scalable, secure, and reliable RAG applications using Knowledge Bases for Amazon Bedrock

AWS Machine Learning

Here are some features which we will cover: AWS CloudFormation support Private network policies for Amazon OpenSearch Serverless Multiple S3 buckets as data sources Service Quotas support Hybrid search, metadata filters, custom prompts for the RetreiveAndGenerate API, and maximum number of retrievals.

APIs 127
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Understanding Spearline Post Dial Delay (PDD)

Spearline

The standard definition of PDD can be defined as: “the time or delay that occurs from the time a number has been dialed, until the caller or called party hears ringing.” ” Most carriers in the telecommunications industry consider anything under seven seconds as an acceptable amount of PDD, with most not troubleshooting it.

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Scale and simplify ML workload monitoring on Amazon EKS with AWS Neuron Monitor container

AWS Machine Learning

Solution overview The Neuron Monitor container solution provides a comprehensive monitoring framework for ML workloads on Amazon EKS, using the power of Neuron Monitor in conjunction with industry-standard tools like Prometheus , Grafana , and Amazon CloudWatch. The Container Insights dashboard also shows cluster status and alarms.

Metrics 96