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Ground truth curation and metric interpretation best practices for evaluating generative AI question answering using FMEval

AWS Machine Learning

This post focuses on evaluating and interpreting metrics using FMEval for question answering in a generative AI application. FMEval is a comprehensive evaluation suite from Amazon SageMaker Clarify , providing standardized implementations of metrics to assess quality and responsibility. Question Answer Fact Who is Andrew R.

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Why Businesses Should Hire Cloud Developers: Key Benefits and Best Practices

CSM Magazine

Faster Deployment Cycles According to a 2023 study, companies using DevOps practices saw a 22% improvement in deployment speed. Developers can also gather real-time metrics, which can guide updates that benefit both employees and customers. Cloud developers configure these features to match your companys security standards.

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Track LLM model evaluation using Amazon SageMaker managed MLflow and FMEval

AWS Machine Learning

Evaluation algorithm Computes evaluation metrics to model outputs. Different algorithms have different metrics to be specified. It keeps records of experiment names, run identifiers, parameter settings, performance metrics, tags, and locations of artifacts. Remember to always following the least privilege access principle.

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Best practices and design patterns for building machine learning workflows with Amazon SageMaker Pipelines

AWS Machine Learning

In this post, we provide some best practices to maximize the value of SageMaker Pipelines and make the development experience seamless. Best practices for SageMaker Pipelines In this section, we discuss some best practices that can be followed while designing workflows using SageMaker Pipelines.

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New year, new CS: 2023 recap and what’s to come in 2024

Totango

Our software provides a robust set of integrations and SuccessBLOCs —our proprietary pre-built customer success programs that are embedded with best practices and contain relevant workflows, reports, automation, customer segments, campaign content, and more to help you achieve critical business outcomes and create focus for your team.

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Best practices for building robust generative AI applications with Amazon Bedrock Agents – Part 1

AWS Machine Learning

This two-part series explores best practices for building generative AI applications using Amazon Bedrock Agents. It’s also a best practice to collect any extra information that would be shared with the agent in a production system. Task completion rate – This measures the success rate of the agent.

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Your guide to generative AI and ML at AWS re:Invent 2023

AWS Machine Learning

Code talks – In this new session type for re:Invent 2023, code talks are similar to our popular chalk talk format, but instead of focusing on an architecture solution with whiteboarding, the speakers lead an interactive discussion featuring live coding or code samples. AWS DeepRacer Get ready to race with AWS DeepRacer at re:Invent 2023!