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Model Engineering

4 articles · Page 1 of 1
TechVision Expert Circle
TechVision Expert Circle
Jul 11, 2026 · Artificial Intelligence

2026 H2 IT Landscape Shifts: From Model Competition to the Three Battlefields of Agents, Data, and Governance

The article argues that in the second half of 2026 the AI race will move from chasing ever larger models to mastering three pragmatic fronts—AI Agent engineering, data‑infrastructure redesign, and robust AI governance—detailing the technical shifts, cost pressures, and compliance demands that will decide which teams succeed.

AI GovernanceAI agentsData Infrastructure
0 likes · 13 min read
2026 H2 IT Landscape Shifts: From Model Competition to the Three Battlefields of Agents, Data, and Governance
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Apr 11, 2026 · Artificial Intelligence

How to Build a Full‑Cycle Model Engineering System for Scalable AI

This article outlines a comprehensive, six‑part model engineering framework that transforms AI capabilities into reusable business functions, defines a stable technical stack, establishes model selection and architecture guidelines, implements rigorous control, data, and training processes, and explains how these layers synergize for reliable, scalable deployment.

AI deploymentData PipelineModel Engineering
0 likes · 27 min read
How to Build a Full‑Cycle Model Engineering System for Scalable AI
PaperAgent
PaperAgent
Mar 29, 2026 · Artificial Intelligence

Why Model Power Isn’t Enough: Inside Anthropic’s Harness for Building Real AI Applications

The article analyzes Anthropic’s Harness framework, showing how combining a planner, a generator model, and an automated evaluator transforms powerful language models into reliable, end‑to‑end AI applications, highlighting the engineering challenges, iterative feedback loops, cost trade‑offs, and evolving design as models improve.

AI agentsAnthropicModel Engineering
0 likes · 9 min read
Why Model Power Isn’t Enough: Inside Anthropic’s Harness for Building Real AI Applications
Bilibili Tech
Bilibili Tech
Sep 29, 2023 · Artificial Intelligence

BILIVQA: Bilibili's No-Reference Video Quality Assessment System

BILIVQA is Bilibili’s deep‑learning, no‑reference video quality assessment system that trains on a proprietary 5,000‑video UGC dataset, extracts spatial and temporal features via MobileNet‑V2 and X3D, uses mixed‑dataset regression for strong generalization, and deploys a GPU‑optimized TensorRT pipeline with percentile‑based scoring for reliable quality monitoring and downstream applications.

BILIVQAModel Engineeringdeep learning
0 likes · 27 min read
BILIVQA: Bilibili's No-Reference Video Quality Assessment System