Perspectives on machine learning, AI engineering, signal processing, and building production-grade systems that create real impact.
Why spreadsheets and ERP min/max rules fail at slow, sporadic spare parts — and the statistical methods (including Croston's) plus machine learning that actually deliver reorder points, safety stock, and projected stockout dates.
Cascaded vs speech-to-speech architectures, the sub-500ms latency budget, $0.40-per-call economics, Whisper vs Deepgram vs Azure, and the Israeli voice AI ecosystem — a production engineering guide.
Iguazio, Dataloop, Neptune.ai, Seldon — the MLOps consolidation wave is real. How to assess your lock-in, plan a migration, and build a stack that survives the next acquisition.
Speech-to-text, voice synthesis, sound event detection, music generation, and real-time edge deployment — what works, what doesn't, and what it costs in 2026.
89% of enterprise agentic AI pilots never reach production. Here's what's really causing the gap — and the five engineering disciplines that separate the 11% that ship.
Small Language Models are reshaping enterprise AI in Israel — delivering frontier-grade results inside your own four walls, without a single token crossing the public internet.
How the three major cloud ML platforms are compressing research-to-production timelines — and what enterprise teams should know when choosing between them.
Nine out of ten enterprise AI agent pilots stall before deployment. Here are the five principles that separate production-grade systems from expensive experiments.
What separates a polished demo from a reliable production agent — architecture patterns, failure modes, evaluation frameworks, and guardrails that matter.
A research-style overview of AI adoption across Israel's key sectors — healthcare, defense, audio tech, ad tech, and finance — and what companies need from specialized AI consulting partners.
A deep dive into the job scheduler powering the world's most advanced AI compute clusters — from exascale Frontier to enterprise on-prem HPC.
Machine learning applied to the full sales pipeline — how predictive lead scoring drove measurable ROI for a major telecom.
How fine-tuning and RAG unlock value from unstructured text, internal documents, and enterprise knowledge bases.
How classical DSP techniques — Fourier transforms, filters, and Nyquist sampling — can be the make-or-break factor in ML success.
A comprehensive look at graph neural networks — from GCNs and GATs to applications in fraud detection, bioinformatics, and recommender systems.
How MLAIA delivers production-grade AI and ML capabilities to startups and enterprises — without the overhead of building an in-house team.