The Problem
Limited data makes new AI configurations risky
Teams risk shipping model or prompt changes blindly, without leveraging their unique, historical and full scale context of a task - not every configuration change suits a specific task.
Companies are investing in AI but are relying on the same public models
It’s challenging for teams to collect high quality and proprietary data that can be used for bespoke training, fine-tuning and improving models.
ML Engineers are being employed to look at spreadsheets
Companies struggle to test their tasks at a large scale. Teams rely on cherry-picked, small scale, labour intensive and manual processes, which lead to guesswork.
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