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Track LLM model evaluation using Amazon SageMaker managed MLflow and FMEval

AWS Machine Learning Blog

Evaluating large language models (LLMs) is crucial as LLM-based systems become increasingly powerful and relevant in our society. Rigorous testing allows us to understand an LLMs capabilities, limitations, and potential biases, and provide actionable feedback to identify and mitigate risk.

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Revolutionizing clinical trials with the power of voice and AI

AWS Machine Learning Blog

This transcription then serves as the input for a powerful LLM, which draws upon its vast knowledge base to provide personalized, context-aware responses tailored to your specific situation. LLM integration The preprocessed text is fed into a powerful LLM tailored for the healthcare and life sciences (HCLS) domain.

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DeepSeek in My Engineer’s Eyes

Towards AI

AI agents, on the other hand, hold a lot of promise but are still constrained by the reliability of LLM reasoning. From an engineering perspective, the core challenge for both lies in improving accuracy and reliability to meet real-world business requirements. They also inspired a bunch of new potentials for ML engineers.

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Enterprise LLM Summit highlights the importance of data development

Snorkel AI

Snorkel AI held its Enterprise LLM Virtual Summit on October 26, 2023, drawing an engaged crowd of more than 1,000 attendees across three hours and eight sessions that featured 11 speakers. Focus on improving data quality and transforming manual data development processes into programmatic operations to scale fine-tuning.

LLM 69
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Enterprise LLM Summit highlights the importance of data development

Snorkel AI

Snorkel AI held its Enterprise LLM Virtual Summit on October 26, 2023, drawing an engaged crowd of more than 1,000 attendees across three hours and eight sessions that featured 11 speakers. Focus on improving data quality and transforming manual data development processes into programmatic operations to scale fine-tuning.

LLM 59
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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

Data quality control: Robust dataset labeling and annotation tools incorporate quality control mechanisms such as inter-annotator agreement analysis, review workflows, and data validation checks to ensure the accuracy and reliability of annotations. Data monitoring tools help monitor the quality of the data.

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Enterprise LLM Summit highlights the importance of data development

Snorkel AI

Snorkel AI held its Enterprise LLM Virtual Summit on October 26, 2023, drawing an engaged crowd of more than 1,000 attendees across three hours and eight sessions that featured 11 speakers. Focus on improving data quality and transforming manual data development processes into programmatic operations to scale fine-tuning.

LLM 52