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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.
In todays fast-paced AI landscape, seamless integration between data platforms and AIdevelopment tools is critical. At Snorkel, weve partnered with Databricks to create a powerful synergy between their data lakehouse and our Snorkel Flow AIdatadevelopment platform.
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.
In todays fast-paced AI landscape, seamless integration between data platforms and AIdevelopment tools is critical. At Snorkel, weve partnered with Databricks to create a powerful synergy between their data lakehouse and our Snorkel Flow AIdatadevelopment platform.
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 data quality control for enhancing LLMs.
The applications also extend into retail, where they can enhance customer experiences through dynamic chatbots and AI assistants, and into digital marketing, where they can organize customer feedback and recommend products based on descriptions and purchase behaviors. The agent sends the personalized email campaign to the end user.
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.
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