When does it make sense to bandit, and when to experiment?
Some of my personal takeaways and highlights from a slightly bigger, seemingly more diverse CODE conference.
You can’t just say “we should take a scientific approach.” That won’t work.
Optimizing search ranking algorithms can drive millions of dollars in revenue. Here's how to A/B test them.
Are mutually exclusive experiments necessary, or dangerous? Here's how to run them in Eppo.
It’s time for experimentation tools to integrate directly with the CMS instead of trying to imitate them
Shailvi Wakhlu explains why machine learning and AI products require experimentation to quantify success.
Eppo makes CUPED widely available, allowing teams to run experiments up to 65% faster than before.
How we designed Eppo Reports to facilitate a shared experimentation journey across an org
Create visually compelling, fully contextualized PDF reports built to communicate experiment results org-wide.
Why experiments are necessary to evaluate LLMs - and how you can easily A/B test between various models with Eppo.
Metrics are the vehicle that drives change in data-driven organizations.
Bayesian and frequentist approaches are fundamentally different, so why do they sometimes yield the same results?
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.