China’s 14th Five-Year Plan and 2035 Vision prioritize AI, prompting a shift from proof‑of‑concept to product deployment; the newly released Model Delivery standard, part of the Model/MLOps maturity model, defines five maturity levels and a reusable pipeline to boost AI engineering across industries.
ModelOps and MLOps are transforming AI engineering by introducing continuous training, integration, and deployment, which streamline development cycles, standardize model management, and enable ongoing monitoring to enhance inference accuracy and maximize the business value generated by AI models.
Gartner's 2021 strategic technology trends highlight the rise of behavior‑driven internet, total experience, privacy‑enhanced computing, distributed cloud, anywhere operation, security mesh, intelligent composable business, AI engineering, and hyper‑automation as key drivers for organizational resilience and growth.
This article provides a comprehensive engineering overview of federated learning, covering its core distributed‑learning concept, Docker‑based deployment, detailed training‑service architecture with validation, scheduling, metadata, and model‑management components, as well as a complete inference framework and workflow for production use.
This article presents a set of charts outlining various learning paths and technologies for aspiring machine‑learning engineers, offering guidance on what to study and why, while also providing community resources for deeper discussion and support.
This article reviews Jia Yangqing’s insights on why deep learning finally succeeded—highlighting the roles of big data and high‑performance computing—while examining its current limitations, emerging challenges, and future opportunities across AI engineering, AutoML, and hardware‑software co‑design.
The article explains that AI engineers need solid architecture knowledge to turn high‑performing algorithms into real‑world solutions, covering four key reasons: algorithm vs. problem solving, on‑site deployment challenges, scalability, and effective team collaboration.
This article traces the evolution of Weibo's recommendation architecture across three major phases—independent 1.0, layered 2.0, and platform‑centric 3.0—detailing the driving business and technical factors, architectural components, advantages, shortcomings, and key outcomes of each stage.
