AI technology has been here for years, but the new wave of AI products and features is game-changing. We covered this, and other topics, at the Seattle AI Meetup.
Building a culture of experimentation benefits greatly from things like reviewing experiments regularly and discussing the results.
Thanks to our support team, our customers can feel like Statsig is a part of their org and not just a software vendor. We want our customers to know that we're here for them.
Migrating experimentation platforms is a chance to cleanse tech debt, streamline workflows, define ownership, promote democratization of testing, educate teams, and more.
Calculating the right sample size means balancing the level of precision desired, the anticipated effect size, the statistical power of the experiment, and more.
The term 'recency bias' has been all over the statistics and data analysis world, stealthily skewing our interpretation of patterns and trends.
A lot has changed in the past year. New hires, new products, and a new office (or two!) GB Lee tells the tale alongside pictures and illustrations:
A deep dive into CUPED: Why it was invented, how it works, and how to use CUPED to run experiments faster and with less bias.
With the statsig-langchain package, developers can set up event logging and experiment assignment in their Langchain application within minutes, unlocking online experimentation in Langchain applications
As a data meeting, buzzwords were well-represented at Data Council 2023; Generative AI, the Semantic Layer, data observability—and did we say AI?
The benefits of feature flagging are plentiful and all relate to the ability to control and gather metrics around individual features rather than an app build as a whole.
To ensure that your feature flagging and experimentation platform RFP meets your business needs, it's important to prepare before diving into writing the RFP.
Join the discussion: How to record important model inputs and outputs such as prompt, model choices, cost, and latency, and also measure performance and interpret results.
Sophisticated caching architecture ensures optimal performance when serving pages, assets, and API responses—and also caches responses for experimentation.
To embrace online testing of AI models, companies need the right set of tools to rapidly iterate on new ideas, learn from failures, and more on.