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Expert: real-time feature stores and ML stream inference
Real-time feature stores are redefining machine learning architectures by enabling continuous and consistent feature computation for streaming inference. This post dives deep into how these systems operate, their architecture, key tools, and emerging trends in operational ML engineering.
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Tools: FastAPI, Docker, BentoML
FastAPI, Docker, and BentoML together form a powerful, production-grade stack for deploying machine learning models. This post explores how each tool fits into the MLOps pipeline, how to integrate them efficiently, and which best practices high-performing teams are using in 2025 to deploy models at scale.
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Empirical: coupling and cohesion analysis
Coupling and cohesion are core indicators of software quality. This article empirically examines how to measure them in Python projects using modern static analysis tools, benchmark data, and continuous integration practices. It connects theory with data-driven insights from 2025 codebases.
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Introduction to technical teaching and mentorship
Technical teaching and mentorship are vital skills for modern engineers. This article introduces the fundamentals of mentoring, communicating complex ideas, and building structured learning paths in engineering environments. It offers practical methods and examples to develop others effectively in 2025 and beyond.
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Topics Everyone Is Talking About No264
Django 6.0 Released • Elites Could Shape Mass Preferences as AI Lowers Persuasion Costs • Unreal Tournament 2004 Is Back • walrus High-Performance Distributed Log Streaming Engine • What I Learned Building a Minimal and Opinionated Coding Agent
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Best practices for reproducible, modular notebooks
This article explores best practices for making notebooks reproducible and modular, focusing on environment management, automation, testing, and CI/CD integration. It presents a detailed guide with code examples, architecture diagrams, and modern tools that empower engineering teams to treat notebooks as reliable, maintainable, and production-ready artifacts.
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Empirical comparison of algorithms
This in-depth article explores empirical benchmarking of algorithms in 2025, highlighting advanced statistical rigor, reproducibility techniques, and modern tooling. It includes examples from sorting and machine learning domains, code samples, pseudographic visualizations, and insights into industry-standard frameworks like Ray, Spark, and MLPerf for real-world performance evaluation.
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