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Machine Learning Engineering

5 articles · Page 1 of 1
Machine Heart
Machine Heart
Aug 7, 2026 · Artificial Intelligence

Frontis-MA1-35B: Open-Source 35B AI-for-AI Model Advances Recursive Self-Improvement

The Frontis-MA1-35B model and the OpenMLE suite, released by a Tsinghua‑affiliated team, demonstrate how execution feedback can be fed back to the model that proposes modifications, achieving significant gains on MLE‑Bench Lite and showing a concrete step toward recursive self‑improvement in AI research.

AI4AIFrontis-MA1Machine Learning Engineering
0 likes · 15 min read
Frontis-MA1-35B: Open-Source 35B AI-for-AI Model Advances Recursive Self-Improvement
PaperAgent
PaperAgent
Aug 7, 2026 · Artificial Intelligence

OpenMLE: Tsinghua’s Self‑Evolving MLE System Pushes 35B Model Past GPT‑5.5

The article introduces OpenMLE, an open‑source full‑stack system for recursive self‑improvement (RSI) research, showing how a 35B Frontis‑MA1 model improves its Medal Average from 39.39% to 71.21% on MLE‑Bench Lite, surpasses GPT‑5.5+Codex, and details the mechanism hierarchy, task‑curation gym, trainable evolution operators, and experimental evidence that training and search gains combine additively.

Evolutionary SearchFrontis-MA1MLE-Bench Lite
0 likes · 19 min read
OpenMLE: Tsinghua’s Self‑Evolving MLE System Pushes 35B Model Past GPT‑5.5
New Oriental Technology
New Oriental Technology
Sep 29, 2021 · Artificial Intelligence

Building and Managing an AI Research Department: Engineering Practices, Organizational Structure, and Educational Applications

This article explores the strategic management and engineering practices required to build an effective AI research department, detailing organizational structures, performance metrics, cost-reduction strategies, and practical educational AI solutions that bridge theoretical algorithms with scalable industrial applications.

AI DevOpsAI R&D ManagementMachine Learning Engineering
0 likes · 23 min read
Building and Managing an AI Research Department: Engineering Practices, Organizational Structure, and Educational Applications
Architecture Digest
Architecture Digest
Aug 15, 2017 · Artificial Intelligence

Why AI Engineers Must Understand Basic Infrastructure: From Big Data to Deep Learning

The article explains why AI engineers need foundational infrastructure knowledge—covering big‑data processing, cloud services, containerization, MapReduce, and deep‑learning platforms—to effectively solve real‑world problems, collaborate with teams, and build scalable, maintainable AI solutions.

AI infrastructureBig DataCloud Computing
0 likes · 14 min read
Why AI Engineers Must Understand Basic Infrastructure: From Big Data to Deep Learning
21CTO
21CTO
Jul 16, 2017 · Artificial Intelligence

Why Every AI Engineer Must Master Infrastructure Basics

In the AI era, engineers need more than cutting‑edge algorithms—they must understand infrastructure, deployment, scalability, and team collaboration, as illustrated by four practical reasons and Google’s architectural breakthroughs that bridge big data, machine learning, and deep learning.

AI infrastructureCloud ComputingGoogle
0 likes · 17 min read
Why Every AI Engineer Must Master Infrastructure Basics