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Python Programming Learning Circle
Python Programming Learning Circle
Mar 31, 2022 · Artificial Intelligence

Comprehensive PyTorch Code Snippets: Configuration, Tensor Operations, Model Definition, Training, and Best Practices

This article provides a thorough collection of commonly used PyTorch code snippets covering environment setup, reproducibility, GPU configuration, tensor manipulation, model building, data preprocessing, training and evaluation loops, custom loss functions, regularization techniques, learning‑rate scheduling, checkpointing, and practical tips for efficient deep‑learning development.

GPUPythonTensor Operations
0 likes · 37 min read
Comprehensive PyTorch Code Snippets: Configuration, Tensor Operations, Model Definition, Training, and Best Practices
DaTaobao Tech
DaTaobao Tech
Mar 22, 2022 · Artificial Intelligence

Online Learning for Real‑Time Ranking in Alibaba's Home‑Decor Channel

The article details Alibaba’s end‑to‑end online‑learning pipeline for real‑time ranking in the Taobao home‑decor channel, covering UT log parsing, full‑feature extraction, ODL sample creation, xDeepCTR model training, and deployment, which yielded up to 7.8% CTR improvement and demonstrates the value of rapid model adaptation.

AlibabaOnline Learningmodel training
0 likes · 15 min read
Online Learning for Real‑Time Ranking in Alibaba's Home‑Decor Channel
JD Retail Technology
JD Retail Technology
Mar 7, 2022 · Artificial Intelligence

AI-Driven UI Testing: Data Collection, Model Development, and Deployment for Mobile App Anomaly Detection

This article presents a comprehensive study on applying AI and deep‑learning techniques to mobile UI testing, covering background challenges, feasibility research, abnormal sample construction, model design, training, evaluation, and future directions for intelligent test automation.

AI testingAnomaly Detectioncomputer vision
0 likes · 13 min read
AI-Driven UI Testing: Data Collection, Model Development, and Deployment for Mobile App Anomaly Detection
DaTaobao Tech
DaTaobao Tech
Feb 17, 2022 · Artificial Intelligence

Unifying Edge AI Training and Deployment: Inside MNN Workbench’s New Workflow

The article outlines how MNN Workbench, Alibaba’s open‑source edge‑AI platform, integrates professional training capabilities, cloud‑based PAI‑DLC resources, multi‑window debugging, and visual Git Flow to streamline end‑to‑end model development, deployment, and iteration for developers of varying expertise.

DebuggingDeploymentEdge AI
0 likes · 10 min read
Unifying Edge AI Training and Deployment: Inside MNN Workbench’s New Workflow
DataFunSummit
DataFunSummit
Oct 18, 2021 · Big Data

Big Data Architecture and Solutions for Financial Scenarios: MMR, Honghu Data Lake, and Yichuang Model Training Platform

This article presents the challenges of big‑data architecture in finance and introduces three integrated solutions—MMR cloud‑based architecture, the Honghu data‑lake management and analysis platform, and the Yichuang model‑training monitoring system—detailing their design, governance, and future outlook.

cloud architecturefinancial technologymodel training
0 likes · 10 min read
Big Data Architecture and Solutions for Financial Scenarios: MMR, Honghu Data Lake, and Yichuang Model Training Platform
DataFunTalk
DataFunTalk
Sep 25, 2021 · Big Data

Big Data Architecture and Solutions at Du Xiaoman Financial: MMR, Honghu Data Lake, and Yichuang Model Training Platform

This article presents Du Xiaoman Financial's big‑data architecture challenges and three integrated solutions—MMR cloud‑based data framework, the Honghu data‑lake management platform, and the Yichuang model‑training monitoring system—detailing their design, governance, low‑threshold usage, and future outlook.

cloud architecturemodel training
0 likes · 12 min read
Big Data Architecture and Solutions at Du Xiaoman Financial: MMR, Honghu Data Lake, and Yichuang Model Training Platform
360 Quality & Efficiency
360 Quality & Efficiency
Sep 3, 2021 · Artificial Intelligence

Model‑Based Audio Denoising Using Deep Learning for Device Quality Evaluation

This article presents a deep‑learning approach that transforms recorded audio into spectrograms, trains a noise‑prediction network (e.g., ResNet, U‑Net, LSTM) to estimate environmental noise, subtracts it in the frequency domain, and reconstructs a cleaner signal for more accurate audio‑device quality assessment.

STFTaudio denoisingdeep learning
0 likes · 11 min read
Model‑Based Audio Denoising Using Deep Learning for Device Quality Evaluation
Tencent Music Tech Team
Tencent Music Tech Team
Jun 1, 2021 · Artificial Intelligence

TDQA: A No-Reference Deep Learning Based Video Quality Assessment Algorithm for Live Streaming

TDQA is a no‑reference, deep‑learning video quality assessment algorithm designed for live‑streaming, built on a large subjectively annotated dataset and an end‑to‑end architecture with fine‑tuned backbones, achieving state‑of‑the‑art accuracy and sub‑second inference for real‑time quality monitoring and pipeline optimization.

Dataset ConstructionNo-ReferenceTDQA
0 likes · 15 min read
TDQA: A No-Reference Deep Learning Based Video Quality Assessment Algorithm for Live Streaming
MaGe Linux Operations
MaGe Linux Operations
May 18, 2021 · Backend Development

Automate Python Notifications for Model Training and Data Transfers

Learn how to use Python to send real-time progress updates and completion alerts via email for long-running tasks such as model training, data processing, and financial modeling, by leveraging the email, smtplib, and MIME libraries to build customizable notifications with images and attachments.

automationdata-processingemail-notifications
0 likes · 13 min read
Automate Python Notifications for Model Training and Data Transfers
DataFunTalk
DataFunTalk
Apr 28, 2021 · Artificial Intelligence

Zero‑Barrier AI Development Training: Automatic Driving Scene Image Segmentation with EasyDL

This article introduces Baidu EasyDL's zero‑threshold AI training camp, focusing on an automatic‑driving scene image‑segmentation case study, covering platform features, model selection, data collection, cleaning, annotation, smart labeling, augmentation, deployment, and a live Q&A session.

AI developmentAutonomous DrivingEasyDL
0 likes · 33 min read
Zero‑Barrier AI Development Training: Automatic Driving Scene Image Segmentation with EasyDL
JD Tech Talk
JD Tech Talk
Nov 13, 2020 · Artificial Intelligence

Practical Engineering Guide to Federated Learning: Deployment, Training, and Inference

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.

AI EngineeringDistributed SystemsDocker
0 likes · 12 min read
Practical Engineering Guide to Federated Learning: Deployment, Training, and Inference
Top Architect
Top Architect
Sep 19, 2020 · Artificial Intelligence

Architecture and Evaluation of Toutiao's Large-Scale Recommendation System

The article details the end‑to‑end architecture of Toutiao's massive recommendation platform, covering system overview, content and user feature extraction, model training, recall strategies, evaluation methodology, and content safety mechanisms, while highlighting practical challenges and engineering solutions.

Content Safetycontent analysisevaluation
0 likes · 18 min read
Architecture and Evaluation of Toutiao's Large-Scale Recommendation System
58 Tech
58 Tech
Aug 12, 2020 · Artificial Intelligence

Guide to Using SPTM (Simple Pre-trained Model) with qa_match for an AI Competition

This article provides a step‑by‑step tutorial on preparing data, pre‑training the SPTM language model, fine‑tuning a text‑classification model, generating predictions, and creating a submission file for the 58.com AI algorithm competition using the open‑source qa_match toolkit.

AINLPSPTM
0 likes · 9 min read
Guide to Using SPTM (Simple Pre-trained Model) with qa_match for an AI Competition
JD Tech Talk
JD Tech Talk
Jul 6, 2020 · Artificial Intelligence

Meta‑Knowledge Transfer for Automated Machine Learning: System Architecture and Methodology

This article proposes a meta‑knowledge transfer framework for AutoML systems, detailing a four‑layer architecture, methods for collecting and updating structured model meta‑knowledge, and strategies that use this knowledge to guide hyper‑parameter search and early‑stop training, thereby improving efficiency and reducing resource consumption.

AutoMLMeta-Knowledgehyperparameter optimization
0 likes · 23 min read
Meta‑Knowledge Transfer for Automated Machine Learning: System Architecture and Methodology
Youzan Coder
Youzan Coder
Jun 17, 2020 · Artificial Intelligence

Sunfish: An Integrated AI Platform for Model Training and Online Service Deployment at Youzan

Sunfish is Youzan’s integrated AI platform that unifies visual drag‑and‑drop model training, notebook‑based algorithm development, automated model management and one‑click publishing with a low‑latency, high‑availability “small‑box” inference service, enabling end‑to‑end deep‑learning workflows from data exploration to online recommendation and risk‑control deployment.

AI PlatformMLOpsMachine Learning
0 likes · 17 min read
Sunfish: An Integrated AI Platform for Model Training and Online Service Deployment at Youzan
Tencent Advertising Technology
Tencent Advertising Technology
May 2, 2020 · Artificial Intelligence

How to Use TI-ONE Built‑in Operators for the 2020 Tencent Advertising Algorithm Competition

This tutorial walks you through creating a TI‑ONE project, ingesting competition data, configuring and training a decision‑tree model with built‑in operators, running the workflow, and downloading and uploading the result files for the 2020 Tencent Advertising Algorithm Competition.

Machine LearningTI-ONEdata pipeline
0 likes · 7 min read
How to Use TI-ONE Built‑in Operators for the 2020 Tencent Advertising Algorithm Competition
DataFunTalk
DataFunTalk
Mar 31, 2020 · Artificial Intelligence

Design and Evolution of the Quality Control Framework for WeChat Look Feature

This article presents the overall design, multi‑dimensional control mechanisms, auxiliary modules, and evolutionary processes of the quality control system used in WeChat's Look feature, detailing data lifecycle, model training, generalization, transfer learning, and continuous anti‑abuse strategies.

Machine Learningcontent moderationmodel training
0 likes · 18 min read
Design and Evolution of the Quality Control Framework for WeChat Look Feature
Qunar Tech Salon
Qunar Tech Salon
Feb 21, 2020 · Artificial Intelligence

Building an End‑to‑End Data‑Model Loop for Alibaba XiaoMi AI Services

The article describes how Alibaba's XiaoMi AI platform constructs a closed‑loop pipeline—from data collection and annotation to model training, evaluation, and real‑time deployment—using multi‑dimensional data processing, visualization, and Spark‑based engines to accelerate iterative improvements and address operational pain points.

AIBig DataSpark
0 likes · 9 min read
Building an End‑to‑End Data‑Model Loop for Alibaba XiaoMi AI Services
360 Quality & Efficiency
360 Quality & Efficiency
Dec 20, 2019 · Artificial Intelligence

Automated APK Test Script Recommendation: Data Processing and Model Training Pipeline

This article describes a complete pipeline for recommending automated test scripts for APK releases, covering CSV data preprocessing, feature encoding, tokenization with pkuseg and jieba, and training various machine‑learning models such as LDA, word2vec, XGBoost, deep neural networks, and multi‑label classifiers to predict script execution order.

APK testingMachine LearningXGBoost
0 likes · 14 min read
Automated APK Test Script Recommendation: Data Processing and Model Training Pipeline
DataFunTalk
DataFunTalk
Nov 4, 2019 · Artificial Intelligence

Standardizing Model Training and Feature Processing in Recommendation Systems

This article describes a standardized workflow for feature collection, configuration, processing, and model training/prediction in large‑scale recommendation systems, using CSV‑based definitions and code generation to ensure consistency between offline training and online serving while reducing manual coding effort.

CTR predictionMachine Learningfeature engineering
0 likes · 14 min read
Standardizing Model Training and Feature Processing in Recommendation Systems
Snowball Engineer Team
Snowball Engineer Team
Oct 17, 2019 · Artificial Intelligence

GPU-Accelerated Model Training Optimizations for Snowball Feed Recommendation System

This article describes the challenges of large‑scale model training for Snowball’s feed recommendation, and details a series of engineering optimizations—including GPU acceleration, multi‑threaded data preparation, TFRecord conversion, compression, and batch‑map reordering—that increased training throughput from 6 k to over 20 k samples per second while reducing CPU and I/O bottlenecks.

GPUTFRecordTensorFlow
0 likes · 15 min read
GPU-Accelerated Model Training Optimizations for Snowball Feed Recommendation System
Didi Tech
Didi Tech
Aug 2, 2019 · Artificial Intelligence

How Didi’s Open‑Source DELTA Platform Accelerates NLP and Speech Model Development

At ACL 2019, Didi unveiled DELTA, an open‑source TensorFlow‑based training framework that unifies NLP and speech tasks, offers configurable pipelines, benchmark models, and seamless deployment, enabling AI developers to quickly move from research to production while leveraging Didi’s extensive open‑source ecosystem.

AI PlatformNLPOpen Source
0 likes · 6 min read
How Didi’s Open‑Source DELTA Platform Accelerates NLP and Speech Model Development
DataFunTalk
DataFunTalk
Oct 26, 2018 · Artificial Intelligence

Large‑Scale Machine Learning and AutoML Techniques for Search Advertising CTR Prediction

The article explains how large‑scale machine learning and AutoML are applied to search advertising click‑through‑rate (CTR) prediction, covering problem definition, feature generation, model training, optimization methods, distributed systems, and recent advances in AutoML with practical case studies.

AutoMLCTR predictionLarge-scale ML
0 likes · 15 min read
Large‑Scale Machine Learning and AutoML Techniques for Search Advertising CTR Prediction
Tencent Advertising Technology
Tencent Advertising Technology
May 14, 2018 · Artificial Intelligence

Tencent Advertising Algorithm Competition Weekly Champion Shares Data Processing, Feature Engineering, Model Training, and Optimization Strategies

The Nanjing University team '每天队员都想改一次名字' shares their winning approach in Tencent Advertising Algorithm Competition, covering data processing, feature engineering, model training techniques, and alternative optimization targets for AUC improvement, and discuss lessons learned from previous year's champion experience.

AUC optimizationMachine LearningTencent Advertising
0 likes · 5 min read
Tencent Advertising Algorithm Competition Weekly Champion Shares Data Processing, Feature Engineering, Model Training, and Optimization Strategies
High Availability Architecture
High Availability Architecture
Dec 11, 2016 · Artificial Intelligence

Why Machine Learning Is Hard: Debugging Challenges and Exponential Difficulty

The article explains that while machine learning has advanced with abundant courses, textbooks, and frameworks, engineers still face hard debugging problems due to algorithmic, implementation, data, and model dimensions, leading to exponential difficulty and long feedback loops that demand intuition and systematic testing.

Artificial IntelligenceDebuggingMachine Learning
0 likes · 8 min read
Why Machine Learning Is Hard: Debugging Challenges and Exponential Difficulty
Architecture Digest
Architecture Digest
Mar 29, 2016 · Artificial Intelligence

Practical Guide to Machine Learning: Problem Modeling, Data Preparation, Feature Engineering, Model Training and Optimization

This article presents a comprehensive, practical guide to applying machine learning in industry, covering problem modeling, data preparation, feature extraction, model training, and optimization, illustrated with a DEAL transaction amount forecasting case study.

Machine Learningdata preparationfeature engineering
0 likes · 17 min read
Practical Guide to Machine Learning: Problem Modeling, Data Preparation, Feature Engineering, Model Training and Optimization
21CTO
21CTO
Oct 16, 2015 · Artificial Intelligence

Mastering Industrial Machine Learning: From Problem Modeling to Model Optimization

This article outlines a complete industrial machine‑learning workflow—starting with problem modeling, through data preparation, feature extraction, model training, and ending with model optimization—illustrated with a real‑world DEAL revenue‑prediction case and practical tips for handling data, features, and model selection.

Industrial ApplicationMachine Learningdata preparation
0 likes · 20 min read
Mastering Industrial Machine Learning: From Problem Modeling to Model Optimization
Meituan Technology Team
Meituan Technology Team
Feb 15, 2015 · Artificial Intelligence

Machine Learning InAction Series: Practical Applications in Industry

This article outlines how Meituan applies machine learning to industrial challenges by detailing the full workflow—from problem modeling and data preparation to feature engineering, model training with algorithms like Logistic Regression and GBDT, and optimization techniques that address underfitting and overfitting for large‑scale deployment.

Industrial ApplicationsMeituandata preparation
0 likes · 15 min read
Machine Learning InAction Series: Practical Applications in Industry
Meituan Technology Team
Meituan Technology Team
Feb 10, 2015 · Artificial Intelligence

Practical Guide to Machine Learning at Meituan: From Problem Modeling to Model Optimization

This guide walks through Meituan’s end‑to‑end offline ML workflow—from problem modeling and data preparation, through feature engineering and normalization, to model selection, training optimization, evaluation, and iterative improvement—emphasizing business alignment, data quality, and practical diagnostics for real‑world deployment.

Industrial Applicationfeature engineeringmodel training
0 likes · 16 min read
Practical Guide to Machine Learning at Meituan: From Problem Modeling to Model Optimization