PySpark Memory Management Explained: Architecture, OOM Causes & TroubleshootingIntroduction Big data plays a critical role in modern Machine Learning (ML) and AI, where processing large-scale datasets efficiently is essential. PySpark enables distributed data processing across m1Oct 20 · 21 min readkuriko-iwai.com
What Actually Happens When You Run a Spark Job?Introduction As datasets grow, processing them efficiently on a single machine can become challenging due to limitations in CPU capacity, memory, and storage throughput. Apache Spark addresses these cOct 18 · 21 min readkuriko-iwai.com
Why Production Machine Learning Demands Causal InferenceIntroduction Standard machine learning (ML) models operate on observational data, making them suitable to answer what will happen under the current data-generating distribution, but failing to predictSep 21 · 21 min readkuriko-iwai.com
How to Solve Time-Varying Confounding & Mediator Bias in Longitudinal ML PipelinesIntroduction Real-world enterprise environments are not single-step games. Once an initial action is taken, the ecosystem doesn’t remain passive—downstream telemetry evolves dynamically, and mid-game 1Sep 20 · 17 min readkuriko-iwai.com
How to Build Reliable RAG: A Deep Dive into 7 Failure Points and Evaluation FrameworksIntroduction Retrieval-Augmented Generation (RAG) is critical for modern AI architecture, serving as an essential framework for building context-aware agents. But moving from a basic prototype to a prMar 29 · 16 min readkuriko-iwai.com
