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Artificial Intelligence Dec 1, 2023 HomeTech

Building a Private Knowledge Base and Large‑Model Platform for Enterprise AI Assistants

This article describes how an enterprise leveraged GPT‑3.5 and other large language models to create a private knowledge base, design prompt engineering, implement plugin extensions, and build a secure, scalable backend and front‑end integration platform that enables AI‑driven customer‑service assistants across multiple business lines.

AIPrompt EngineeringPlugin ArchitecturewebsocketLarge Language ModelPrivate Knowledge Base

Artificial Intelligence Oct 10, 2023 政采云技术

Predicting Membership Purchase with Logistic Regression: Feature Engineering, Model Training, Evaluation, and Deployment

This article presents a complete workflow for predicting whether users will purchase a membership using logistic regression, covering data collection, feature selection, handling imbalanced samples, model training, hyper‑parameter tuning, threshold optimization, evaluation metrics such as accuracy, precision, recall, AUC, lift, and finally deployment on a big‑data platform with PySpark.

Big Datafeature engineeringmodel evaluationlogistic regressionmembership prediction

Artificial Intelligence Oct 7, 2023 DataFunSummit

MLOps Implementation in Network Intelligence: Jiutian Platform Overview

This article presents the Jiutian Network Intelligence platform’s MLOps implementation at China Mobile, detailing its AI engineering workflow, platform functional and technical architecture, technology selections, model deployment, monitoring, and operational challenges, and shares insights on scaling AI services across 31 provinces.

Cloud NativeMLOpsPlatform ArchitectureAI EngineeringNetwork Intelligence

Artificial Intelligence Sep 13, 2023 HelloTech

AI Platform‑Powered Automated Ticket Routing: Modeling Workflow, Feature Engineering, and Intent Recognition

The Haro AI platform automates customer‑service ticket routing by applying a four‑step pipeline—feature processing, model training, evaluation, and deployment—using BERT/ALBERT‑based intent recognition, configurable feature storage, AutoML or expert modes, and Faas‑style deployment, as demonstrated in the Universal Ticket System case study, dramatically improving accuracy and efficiency.

feature engineeringmodel trainingBERTIntent RecognitionAI platformALBERTautomated ticket routing

Backend Development Sep 12, 2023 DataFunSummit

Xiaohongshu Recommendation Engineering Architecture: Graph Architecture, Hot Deployment, and Practices

This article presents Xiaohongshu's evolving recommendation engineering architecture, detailing its modular backend design, graph-based Ark framework, hot deployment mechanisms, and the challenges and solutions for scaling personalized content delivery in a fast‑growing mobile platform.

backend architecturerecommendation systemXiaohongshugraph computingscalable systemshot deployment

Frontend Development Sep 4, 2023 Rare Earth Juejin Tech Community

Reflections on Frontend Engineering Practices at Meituan: Tooling, Standards, and SDK Projects

This article recounts three years of frontend engineering work at Meituan, detailing the evolution of frontend technology, the adoption of engineering standards, tool and platform development for backend projects, and the specific engineering practices applied to SDK development, while also sharing lessons learned and future directions.

engineeringFrontendsdkdevopstoolingmeituan

Operations Aug 28, 2023 DeWu Technology

Real-time Data Warehouse Business-Side Chaos Engineering Practice

The article describes how a real‑time data warehouse supporting ad‑delivery metrics adopts both technical and business‑side chaos‑engineering, using red‑blue team drills to inject faults, monitor indicator anomalies, and refine response procedures, thereby enhancing early risk detection, system resilience, and overall data stability for the advertising platform.

monitoringrisk managementbackend developmentchaos engineeringdata qualitydata warehousingops

Artificial Intelligence Aug 22, 2023 HelloTech

AI Platform Architecture and Automation in Machine Learning

An end‑to‑end AI platform integrates feature processing, model training, deployment, and decision orchestration across offline and online layers, leveraging automated pipelines such as AutoML (feature engineering, hyper‑parameter optimization, neural architecture search) built on Ray Tune and NNI, which have already boosted CTR in real‑world advertising and aim to make every user an algorithm engineer.

Machine Learningdeep learningautomationAutoMLAI platformHPO

Artificial Intelligence Aug 9, 2023 HelloTech

AutoML in Hello's AI Platform and Quarkc: Building the Next‑Generation Front‑End Component Engine

At the 2023 SECon Global Software Engineering Innovation Summit in Shanghai, Hello’s technology team will showcase how its AI platform leverages AutoML to streamline model development across intelligent mobility services, and how the Quarkc engine uses Web Components to create cross‑stack, framework‑agnostic front‑end components.

Software engineeringWeb ComponentsConferenceAutoMLAI platformFront-end components

Operations May 12, 2023 DevOps

Evolution of Chaos Engineering at Netflix: From Chaos Monkey to ChAP

This article examines how Netflix has progressively refined its chaos engineering practices—from the early Chaos Monkey tool to the sophisticated Chaos Automation Platform (ChAP)—to improve system resilience, automate experiments, and safely validate changes in large‑scale microservice environments.

Cloud NativeMicroservicesChaos EngineeringReliabilityFault InjectionNetflix

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