KIKuriko Iwaiinkuriko-iwai.com·5d ago · 19 min readDemystifying Apache Spark - Core Mechanics & RDD Execution GuideIntroduction Processing terabyte-scale datasets on a single machine is notoriously challenging due to severe memory constraints, disk I/O bottlenecks, and physical hardware limits. Apache Spark overco00
KIKuriko Iwaiinkuriko-iwai.com·Sep 21 · 21 min readWhy 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 predict00
KIKuriko Iwaiinkuriko-iwai.com·Sep 20 · 17 min readHow 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 10
KIKuriko Iwaiinkuriko-iwai.com·Apr 5 · 14 min readArchitecting Semantic Chunking Pipelines for High-Performance RAGIntroduction While Retrieval-Augmented Generation (RAG) has become the gold standard for grounding AI in private data, the quality of its output is only as good as the information it retrieves. To ens00
KIKuriko Iwaiinkuriko-iwai.com·Mar 29 · 16 min readHow 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 pr00
