This post is co-authored by Maha Arunachalam (devops engineer at Pluralsight) and in collaboration with Theo Cowan (devops engineer); Connor McKay (machine learning engineer); and Jeff Lewis & Zander Nickle (streaming software engineers)
If you’ve been around the dataverse a bit, you’ve likely heard of Apache Kafka. Perhaps you’ve even played with it. Overall, Kafka (the tool, not the philosopher) provides a horizontally scalable stream-processing engine that offers highly performant, fault-tolerant, real-time data feeds.
TLDR: Pluralsight, a SaaS company, is AB experimenting using in-house solutions built on user feedback and consistency
Step 1: Start Analyzing Actions Scientifically (SaaS)
In a previous post, we wrote about our efforts to make Pluralsight product development more data driven. For context, one critical highlight of that post is that we found we needed to establish better metric standards and a central repository for all of our AB experiments. Zooming out a bit, AB experiments historically were based around the needs of e-commerce companies, who were focused on event-based metrics like…
I’m a Principal Data Scientist at Pluralsight, where we’re democratizing tech skills.