We Lost 4 Hours to Ray Serve Misconfiguration (Here’s What We Changed)
Our Ray Serve deployment melted at 2 AM. These three autoscaling parameters fixed it, and we haven’t had…
Our Multi-Touch Attribution Was Wrong for 18 Months (DoWhy Showed Us Why)
Traditional attribution confuses correlation with causation. Here’s how to use DoWhy’s causal inference to find which channels actually…
We Built the Perfect MLOps Diagram (Then Reality Broke Everything)
That beautiful MLOps diagram on Confluence? It’s probably wrong. Here’s what actually survives production pressure, from someone who’s…
Why Our Beautiful 7-Day Aggregation Feature Was Useless at Serving Time
Our fraud model dropped from 97% to 60% accuracy in production. The culprit? Features that took hours to…
I Thought Redis Was Our Bottleneck (I Was Completely Wrong)
We blamed Redis for our Black Friday meltdown. Flame graphs told a different story. Here’s where our feature…
I Watched a Team Spend $40K Retraining a Model That Wasn’t Broken
A team spent $40K retraining a model that wasn’t broken. They confused data drift with concept drift. Here’s…
I Made the Mistake of Trusting Vendor Benchmarks for Our ML Pipeline (Never Again)
In 2026, the gap between a vendor’s glossy performance chart and your production reality isn’t just a margin…
After 47 Post-Mortems, I Can Predict the Problem Before the Meeting Starts
47 post-mortems, 7 patterns. I can predict why your data pipeline failed before the meeting starts. #3 is…
I Got Tired of Vendor-Reported MLOps Stats (So I Built My Own Dataset)
Vendor stats told one story, our production telemetry told another. So I built my own MLOps dataset from…
We Had a 1.2 TB Feature Table in Postgres (Here’s Why That Was a Terrible Idea)
We cut ML deployment from 6 weeks to 4 hours after ditching our 1.2 TB Postgres feature table….
