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Reduce ML training costs with Amazon SageMaker HyperPod

AWS Machine Learning - AI

Training a frontier model is highly compute-intensive, requiring a distributed system of hundreds, or thousands, of accelerated instances running for several weeks or months to complete a single job. For example, pre-training the Llama 3 70B model with 15 trillion training tokens took 6.5 During the training of Llama 3.1

Training 116
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Why GreenOps will succeed where FinOps is failing

CIO

Capital One built Cloud Custodian initially to address the issue of dev/test systems left running with little utilization. Architects must combine functional requirements with multiple other long-term requirements to build sustainable systems. Standardized metrics. Overemphasis on tools, budgets and controls.

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LLM benchmarking: How to find the right AI model

CIO

There are two main approaches: Reference-based metrics: These metrics compare the generated response of a model with an ideal reference text. Reference-free metrics: These metrics evaluate the quality of a generated text independently of a reference. This approach enables new possibilities that go beyond classic metrics.

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How BQA streamlines education quality reporting using Amazon Bedrock

AWS Machine Learning - AI

Manually reviewing and processing this information can be a challenging and time-consuming task, with a margin for potential errors. The Education and Training Quality Authority (BQA) plays a critical role in improving the quality of education and training services in the Kingdom Bahrain.

Education 115
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Accelerating insurance policy reviews with generative AI: Verisk’s Mozart companion

AWS Machine Learning - AI

Verisk has a governance council that reviews generative AI solutions to make sure that they meet Verisks standards of security, compliance, and data use. Verisk also has a legal review for IP protection and compliance within their contracts. Verisk conducted multiple rounds of human evaluation of the generated results.

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Beyond automation: Realizing the full potential of agentic AI in the enterprise

CIO

This paper explores the emergence of agentic AI in the enterprise through three key themes: Core properties of a true agentic system. Practical pathways for integrating agentic AI into existing enterprise environments, particularly those constrained by compliance or legacy systems. Network-enabled. Semi-autonomous. Collaborative.

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Model customization, RAG, or both: A case study with Amazon Nova

AWS Machine Learning - AI

Demystifying RAG and model customization RAG is a technique to enhance the capability of pre-trained models by allowing the model access to external domain-specific data sources. Unlike fine-tuning, in RAG, the model doesnt undergo any training and the model weights arent updated to learn the domain knowledge. Choose Next.