Eppo's best-in-class diagnostics ensure that your experiments yield trustworthy, actionable results.
Azadeh Moghtaderi explains why only A/B testing can gauge the magnitude and impact of AI/ML models.
How do you get from 10 experiments to 1000? Here are some practical tips to scale your velocity.
There is a gold standard for evaluating AI models: Comparing models in AB experiments against business metrics.
As the cost of implementing ideas goes to zero, evaluating ideas becomes the bottleneck
You can now combine the most powerful experimentation tool with the best-in-class model deployment platform.
Eppo's new pipeline architecture reduces both warehouse costs and pipeline run-times. Here's how we did it.
Data leader Rick Saporta explains the role and purpose of data teams: to make better decisions.
Now, you have the ability to query Eppo’s internals with one click.
How to understand statistical power, multiple testing, and peeking by leveraging the definition of a p-value.
Terrific writeups get your leadership and colleagues excited about the value your team is delivering.
Companies that use the end-to-end Lakehouse Platform can now run experiments with Eppo.
Here's how to ensure you’re making experiment decisions based on solid data foundations.
Eppo’s new progress bar gives your team a much-improved tool for understanding your level of certainty.
Growth Advisor Hila Qu explains how experimentation (plus strategy) is key to finding growth opportunities.
A Special Advisor to the Los Angeles Dodgers goes behind the scenes of how sports teams use Data Science.
How Eppo serves feature flags to a huge customer base while keeping a light infrastructure footprint of our own.
InvestInData is an angel investor collective composed of 30+ leading data executives.
Feature flagging and randomization are two prerequisites for running experiments. Here's how they work.
With feature flagging, Eppo becomes the first experimentation tool that lets teams operate in a variety of ways.