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 deploymentModel TrainingOperations

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

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.

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

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.

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0 likes · 27 min read

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