Over the last couple of months, our customer conversations on AI experimentation have increased dramatically (as you may have guessed), so we decided the time is now to dive deep into how experimentation can only benefit the development of AI features.
Enjoy this on-demand viewing and we hope you can join us live in the future!
Got AI on the brain? Statsig can help you run experiments with AI apps, using our recent launch of Statbot. During this Learning Lab we'll teach you how to record important model inputs and outputs, such as prompt, model choices, cost, and latency.
Additionally, we'll provide some tips on how to measure your application's performance and interpret the results to make data-driven decisions.
Hypothesis Testing often confuses data scientists due to mixed teachings on p-values and significance testing. This article clarifies 10 key concepts with visuals and intuitive explanations.
I discussed 8 A/B testing mistakes with Allon Korem (Bell Statistics) and Tyler VanHaren (Statsig). Learn fixes to improve accuracy and drive better business outcomes.
Introducing Differential Impact Detection: Identify how different user groups respond to treatments and gain useful insights from varied experiment results.
Identify power users to drive growth and engagement. Learn to pinpoint and leverage these key players with targeted experiments for maximum impact.
Simplify data pipelines with Statsig. Use SDKs, third-party integrations, and Data Warehouse Native Solution for effortless data ingestion at any stage.
Learn how we use Statsig to enhance our NestJS API servers, reducing request processing time and CPU usage through performance experiments.