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In the generative AI or traditional AIdevelopment cycle, dataingestion serves as the entry point. Here, raw data that is tailored to a company’s requirements can be gathered, preprocessed, masked and transformed into a format suitable for LLMs or other models.
The integration between the Snorkel Flow AIdatadevelopment platform and AWS’s robust AI infrastructure empowers enterprises to streamline LLM evaluation and fine-tuning, transforming raw data into actionable insights and competitive advantages. Here’s what that looks like in practice.
Addressing this challenge requires a solution that is scalable, versatile, and accessible to a wide range of users, from individual researchers to large teams working on the state-of-the-art side of AIdevelopment. Existing research emphasizes the significance of distributed processing and dataquality control for enhancing LLMs.
The integration between the Snorkel Flow AIdatadevelopment platform and AWS’s robust AI infrastructure empowers enterprises to streamline LLM evaluation and fine-tuning, transforming raw data into actionable insights and competitive advantages. Heres what that looks like in practice.
Snorkel AI has teamed with Snowflake to help our shared customers transform raw, unstructured data into actionable, AI-powered insights. Users are able to rapidly improve training dataquality and model performance using integrated error analysis to develop highly accurate and adaptable AI applications.
Snorkel AI has teamed with Snowflake to help our shared customers transform raw, unstructured data into actionable, AI-powered insights. Users are able to rapidly improve training dataquality and model performance using integrated error analysis to develop highly accurate and adaptable AI applications.
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