MLOps

SHAP KernelExplainer takes ~30 ms per prediction (even with a small background) A neuro-symbolic model generates explanations contained in the forward pass in 0.9 ms That’s a 33× speedup with deterministic outputs Fraud recall...

has been in production two months. Accuracy is 92.9%. Then transaction patterns shift quietly. By the point your dashboard turns red, accuracy has collapsed to 44.6%. Retraining takes six hours—and wishes labeled data you won’t have...

, every metric looked perfect.RWSS = 1.000. Output probabilities unchanged. No labels moved.All the pieces said “all clear.” Then the alert fired anyway. Window 3: severity=warning RWSS=1.000 fired=True ← FIDI Z fires here The model’s predictions didn’t...

finally work. They call tools, reason through workflows, and really complete tasks. Then the first real API bill arrives. For a lot of teams, that’s the moment the query appears: “Should we just run this ourselves?” The excellent...

yourself how real machine learning products actually run in major tech corporations or departments? If yes, this text is for you 🙂 Before discussing scalability, please don’t hesitate to read my first article on...

Introduction a continuous variable for 4 different products. The machine learning pipeline was in-built Databricks and there are two major components.  Feature preparation in SQL with serverless compute. Inference on an ensemble of several hundred models using...

project involving the construct of propensity models to predict customers’ prospective purchases, I encountered feature engineering issues that I had seen quite a few times before. These challenges might be broadly classified into two categories: 1)...

introduced Gaudi accelerators to Amazon’s EC2 DL1 instances, we faced a challenge that threatened your complete deployment. The performance numbers were not only disappointing; they were disastrous. Models that required training effectively were...