Remove Analysis Remove APIs Remove Metrics
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LLM-as-a-judge on Amazon Bedrock Model Evaluation

AWS Machine Learning

This approach allows organizations to assess their AI models effectiveness using pre-defined metrics, making sure that the technology aligns with their specific needs and objectives. Expert analysis : Data scientists or machine learning engineers analyze the generated reports to derive actionable insights and make informed decisions.

Metrics 112
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Build generative AI applications quickly with Amazon Bedrock IDE in Amazon SageMaker Unified Studio

AWS Machine Learning

They have structured data such as sales transactions and revenue metrics stored in databases, alongside unstructured data such as customer reviews and marketing reports collected from various channels. This includes setting up Amazon API Gateway , AWS Lambda functions, and Amazon Athena to enable querying the structured sales data.

APIs 107
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Build a multi-tenant generative AI environment for your enterprise on AWS

AWS Machine Learning

It also uses a number of other AWS services such as Amazon API Gateway , AWS Lambda , and Amazon SageMaker. API Gateway is serverless and hence automatically scales with traffic. API Gateway also provides a WebSocket API. Incoming requests to the gateway go through this point.

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Build a video insights and summarization engine using generative AI with Amazon Bedrock

AWS Machine Learning

All of this data is centralized and can be used to improve metrics in scenarios such as sales or call centers. For integration between services, we use API Gateway as an event trigger for our Lambda function, and DynamoDB as a highly scalable database to store our customer details.

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Empower your generative AI application with a comprehensive custom observability solution

AWS Machine Learning

Observability refers to the ability to understand the internal state and behavior of a system by analyzing its outputs, logs, and metrics. Evaluation, on the other hand, involves assessing the quality and relevance of the generated outputs, enabling continual improvement.

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Benchmarking Amazon Nova and GPT-4o models with FloTorch

AWS Machine Learning

How do Amazon Nova Micro and Amazon Nova Lite perform against GPT-4o mini in these same metrics? Amazon Bedrock APIs make it straightforward to use Amazon Titan Text Embeddings V2 for embedding data. Vector database FloTorch selected Amazon OpenSearch Service as a vector database for its high-performance metrics.

Benchmark 114
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Unlock cost savings with the new scale down to zero feature in SageMaker Inference

AWS Machine Learning

Use faster auto scaling metrics – Take advantage of more granular auto scaling metrics like ConcurrentRequestsPerCopy to more accurately monitor and react to changes in inference traffic. It’s a dynamic policy that adjusts the number of copies based on a specified metric, such as CPU utilization or request count.

APIs 110