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AI Explorer
AI Explorer
Mar 28, 2026 · Artificial Intelligence

UCSD’s AIBuildAI Tops OpenAI Ranking, Signaling a Silent AI Development Revolution

UCSD’s AIBuildAI agent achieved first place on OpenAI’s benchmark by automatically designing, coding, training, and tuning a complete AI model without human engineers, a breakthrough that suggests a shift from tool‑assisted AI creation to fully autonomous AI‑generated AI, raising both efficiency gains and new interpretability challenges.

AI automationAI development paradigmAI interpretability
0 likes · 6 min read
UCSD’s AIBuildAI Tops OpenAI Ranking, Signaling a Silent AI Development Revolution
Baobao Algorithm Notes
Baobao Algorithm Notes
Mar 20, 2026 · Artificial Intelligence

Can AI Self‑Iterate? Inside MiniMax M2.7’s Self‑Improving Magic

The article examines MiniMax M2.7’s claim of self‑iteration, its impressive Kaggle record, and a series of technical tests—including code refactoring, real‑time chart generation, futures backtesting, business analysis, PPT creation, and news tracking—to evaluate the model’s practical AI self‑evolution capabilities.

AIAutoMLKaggle
0 likes · 8 min read
Can AI Self‑Iterate? Inside MiniMax M2.7’s Self‑Improving Magic
Aikesheng Open Source Community
Aikesheng Open Source Community
Dec 16, 2025 · Databases

How to Build Predictive and Generative AI Apps with MySQL AI

MySQL AI adds built‑in LLMs, embeddings, vector storage, AutoML and a graphical console to on‑premise MySQL, enabling developers to create predictive and generative AI applications—including fraud detection, semantic search, RAG and NL2SQL—without external vector databases or GPUs.

AutoMLMySQL AIPredictive AI
0 likes · 15 min read
How to Build Predictive and Generative AI Apps with MySQL AI
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Oct 14, 2025 · Artificial Intelligence

How TS‑Agent Uses LLMs and Reflective Feedback to Automate Financial Time‑Series Modeling

TS‑Agent is a modular LLM‑driven framework that formalizes financial time‑series modeling as a three‑stage iterative decision process, leveraging structured knowledge bases, dynamic memory, and a feedback‑driven code‑editing loop to outperform AutoML baselines in accuracy, robustness, and auditability.

AutoMLFeedback LoopKnowledge Base
0 likes · 12 min read
How TS‑Agent Uses LLMs and Reflective Feedback to Automate Financial Time‑Series Modeling
Architects Research Society
Architects Research Society
Sep 12, 2025 · Artificial Intelligence

Master Generative AI: From Core Concepts to Advanced Techniques

This comprehensive guide walks you through generative AI fundamentals—including transformers, diffusion models, large language models, and multimodal systems—then explores practical API usage with OpenAI, Hugging Face, and Vertex AI, followed by model fine‑tuning, LoRA, knowledge injection, and advanced topics such as model distillation, prompt chaining, AutoML, tool integration, and retrieval‑augmented generation.

AutoMLPrompt engineeringmodel fine-tuning
0 likes · 3 min read
Master Generative AI: From Core Concepts to Advanced Techniques
Swan Home Tech Team
Swan Home Tech Team
Aug 27, 2025 · Artificial Intelligence

Why AutoGluon’s Smart Model Team Beats Traditional Tuning in Real-World AI

This guide explains how AutoGluon leverages bagging, cross‑validation, and stacked ensembling to automatically train and combine dozens of models, provides step‑by‑step installation and usage instructions for tabular, time‑series, and multimodal tasks, and shows practical deployment examples for industry scenarios.

AutoGluonAutoMLModelEnsembling
0 likes · 21 min read
Why AutoGluon’s Smart Model Team Beats Traditional Tuning in Real-World AI
Python Programming Learning Circle
Python Programming Learning Circle
Jun 30, 2025 · Artificial Intelligence

Choosing the Right AutoML Library: In‑Depth Python Comparisons & Use‑Cases

This article reviews the evolution of AutoML, explains its core principles, compares major Python AutoML libraries with code examples, provides a decision‑making framework and benchmark results, and offers practical guidance on selecting the most suitable tool for different machine‑learning projects.

AutoMLBenchmarkModel Selection
0 likes · 15 min read
Choosing the Right AutoML Library: In‑Depth Python Comparisons & Use‑Cases
JD Retail Technology
JD Retail Technology
May 19, 2025 · Artificial Intelligence

How JD’s Omniforce Boosts Large Model Efficiency with Cloud‑Edge Collaboration

The JD Exploration Institute paper introduces Omniforce, a human‑centered, cloud‑edge collaborative AutoML system that uses model distillation, dynamic data governance, Bayesian‑optimized training, and edge deployment to cut large‑model training costs by 70% and improve inference speed by 30%, powering the JoyBuild platform for broader AI adoption.

AI efficiencyAutoMLJoyBuild
0 likes · 6 min read
How JD’s Omniforce Boosts Large Model Efficiency with Cloud‑Edge Collaboration
php Courses
php Courses
May 15, 2025 · Artificial Intelligence

Why Python Dominates Data Analysis and Machine Learning: Core Tools, Full‑Stack Solutions, and Learning Path

This article explains why Python has become the leading language for data analysis and machine learning, outlines the essential libraries and frameworks, provides practical code examples, describes typical application scenarios, suggests a staged learning roadmap, and forecasts future trends such as AutoML and federated learning.

AutoMLPyTorchPython
0 likes · 6 min read
Why Python Dominates Data Analysis and Machine Learning: Core Tools, Full‑Stack Solutions, and Learning Path
Tencent Advertising Technology
Tencent Advertising Technology
Dec 27, 2024 · Artificial Intelligence

Tencent's AutoML Research for Advertising Recommendation Systems

This article outlines Tencent's AutoML research, presenting several recent papers that introduce novel neural architecture search, feature selection, pooling, embedding size, and hyper‑parameter optimization techniques to improve the efficiency, accuracy, and scalability of large‑scale advertising recommendation systems.

AutoMLEmbedding Size SearchNeural Architecture Search
0 likes · 10 min read
Tencent's AutoML Research for Advertising Recommendation Systems
Python Programming Learning Circle
Python Programming Learning Circle
Sep 12, 2024 · Artificial Intelligence

Curated List of Python Libraries for Data Visualization, Machine Learning, and Development

This article compiles a comprehensive, subjectively curated collection of Python libraries for data visualization, machine learning, deep learning, AutoML, model interpretability, resource monitoring, and debugging, providing brief descriptions and links to each tool for developers and researchers.

AutoMLPythondata-visualization
0 likes · 9 min read
Curated List of Python Libraries for Data Visualization, Machine Learning, and Development
DataFunTalk
DataFunTalk
Oct 20, 2023 · Artificial Intelligence

Building the ATLAS Automated Machine Learning Platform at Du Xiaoman: Architecture, Practices, and Optimizations

This article describes how Du Xiaoman tackled the high cost, instability, and long cycles of AI algorithm deployment by building the ATLAS automated machine learning platform, detailing its four‑stage workflow, component platforms, scaling and efficiency techniques, and practical Q&A for practitioners.

AI deploymentAutoMLData Parallelism
0 likes · 22 min read
Building the ATLAS Automated Machine Learning Platform at Du Xiaoman: Architecture, Practices, and Optimizations
DataFunTalk
DataFunTalk
Aug 25, 2023 · Artificial Intelligence

Advances in Graph Neural Architecture Search: GASSO, DHGAS, GAUSS, GRACES, G‑RNA and the AutoGL Library

This article surveys recent progress in automated graph machine learning, covering graph neural architecture search techniques such as GASSO, DHGAS, GAUSS, GRACES, and G‑RNA, discusses scalability and robustness challenges, and introduces the open‑source AutoGL library and the NAS‑Bench‑Graph benchmark.

AutoGLAutoMLNeural Architecture Search
0 likes · 19 min read
Advances in Graph Neural Architecture Search: GASSO, DHGAS, GAUSS, GRACES, G‑RNA and the AutoGL Library
HelloTech
HelloTech
Aug 22, 2023 · Artificial Intelligence

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.

AI PlatformAutoMLDeep Learning
0 likes · 8 min read
AI Platform Architecture and Automation in Machine Learning
DataFunTalk
DataFunTalk
Aug 22, 2023 · Artificial Intelligence

Building Complex Distributed Systems with Ray: An AutoML Case Study and Cloud‑Native Deployment

This article explains how the Ray distributed computing engine simplifies the design, deployment, and operation of complex cloud‑native distributed systems—illustrated through an AutoML service example—by detailing system complexity, Ray’s core concepts, resource customization, runtime environments, monitoring, and ecosystem integrations.

AIAutoMLCloud Native
0 likes · 26 min read
Building Complex Distributed Systems with Ray: An AutoML Case Study and Cloud‑Native Deployment
HelloTech
HelloTech
Aug 9, 2023 · Artificial Intelligence

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.

AI PlatformAutoMLFront-end components
0 likes · 4 min read
AutoML in Hello's AI Platform and Quarkc: Building the Next‑Generation Front‑End Component Engine
DataFunSummit
DataFunSummit
Feb 22, 2023 · Artificial Intelligence

AutoML Overview: Hyperparameter Optimization, Automatic Feature Engineering, and Neural Architecture Search on Alibaba PAI

This article introduces AutoML, explaining how it automates data cleaning, feature engineering, model selection, hyper‑parameter optimization, and neural architecture search, and showcases Alibaba PAI's implementations of HPO, AutoFE, and NAS with practical case studies and performance results.

Alibaba PAIAutoMLNeural Architecture Search
0 likes · 15 min read
AutoML Overview: Hyperparameter Optimization, Automatic Feature Engineering, and Neural Architecture Search on Alibaba PAI
DataFunTalk
DataFunTalk
Feb 18, 2023 · Artificial Intelligence

Building the ATLAS Automated Machine Learning Platform at Du Xiaoman: Architecture, Optimization, and Practical Insights

This article details Du Xiaoman's development of the ATLAS automated machine learning platform, covering business scenarios, AI algorithm deployment challenges, the end‑to‑end production workflow, platform components such as annotation, data, training and deployment, as well as optimization techniques like AutoML, meta‑learning, NAS, and large‑scale parallelism, concluding with lessons learned and future directions.

AI deploymentAutoMLMachine Learning Platform
0 likes · 20 min read
Building the ATLAS Automated Machine Learning Platform at Du Xiaoman: Architecture, Optimization, and Practical Insights
SQB Blog
SQB Blog
Nov 18, 2022 · Artificial Intelligence

Boosting AI Model Development with Alibaba's EasyModeling Framework

This article introduces the EasyModeling framework built on Alibaba Cloud's PAI platform, detailing its modular design, high reusability, integration with deep‑learning libraries, automated hyper‑parameter tuning, deployment scenarios, and a real‑world case study using RoBERTa for dish‑name standardization, demonstrating significant performance gains.

AI modelingAlibaba CloudAutoML
0 likes · 13 min read
Boosting AI Model Development with Alibaba's EasyModeling Framework
DataFunTalk
DataFunTalk
Sep 7, 2022 · Artificial Intelligence

Pluto: OPPO’s AutoML Tool for Hardware‑Aware Model Compression and Deployment

This article introduces OPPO’s self‑developed AutoML platform Pluto, explains why automated machine learning and model compression are essential for industrial AI, describes Pluto’s hardware‑aware and uniform algorithm framework, showcases typical applications such as video super‑resolution, and provides a detailed Q&A on its methodology and performance.

AutoMLHardware‑AwareNeural Architecture Search
0 likes · 15 min read
Pluto: OPPO’s AutoML Tool for Hardware‑Aware Model Compression and Deployment
DataFunSummit
DataFunSummit
May 18, 2022 · Artificial Intelligence

Automated Knowledge Graph Representation Learning: From Triples to Subgraphs

This talk introduces automated knowledge graph representation learning, covering background, key techniques such as triple‑based, path‑based and subgraph‑based models, AutoML‑driven model search (AutoSF, Interstellar, RED‑GNN), evaluation metrics, and future research directions in AI.

AutoMLEmbeddingKnowledge Graph
0 likes · 21 min read
Automated Knowledge Graph Representation Learning: From Triples to Subgraphs
DataFunTalk
DataFunTalk
May 8, 2022 · Artificial Intelligence

Automated Knowledge Graph Representation Learning: From Triples to Subgraphs

This talk introduces the background, key directions, and model designs for automated knowledge‑graph representation learning, covering triple‑based, path‑based, and subgraph‑based approaches, the role of AutoML in searching optimal bilinear scoring functions, and future research challenges such as scalability, inductive inference, and domain‑specific applications.

AutoMLEmbeddingKnowledge Graph
0 likes · 20 min read
Automated Knowledge Graph Representation Learning: From Triples to Subgraphs
GuanYuan Data Tech Team
GuanYuan Data Tech Team
May 5, 2022 · Artificial Intelligence

Why FLAML Is the Fast, Lightweight AutoML Framework You Should Try

This article introduces Microsoft’s FLAML, a fast and lightweight AutoML library, explains its design principles, cost‑aware search strategy, key observations, properties, and experimental results, and provides practical code examples for integrating FLAML into Python machine‑learning workflows.

AutoMLCost-aware SearchFLAML
0 likes · 15 min read
Why FLAML Is the Fast, Lightweight AutoML Framework You Should Try
DataFunSummit
DataFunSummit
May 1, 2022 · Artificial Intelligence

Intelligent Risk Control Platform: Design Background, Full‑Cycle Strategy and Model Management, and Business Architecture

This article presents a comprehensive overview of an intelligent risk control middle‑platform, covering its design background, the five‑characteristics and "five‑all double‑core" concept, full‑cycle strategy and model lifecycle management, business architecture, and real‑world application cases, highlighting the integration of rule‑based and AI‑driven decision engines.

AIAutoMLModel Management
0 likes · 13 min read
Intelligent Risk Control Platform: Design Background, Full‑Cycle Strategy and Model Management, and Business Architecture
GuanYuan Data Tech Team
GuanYuan Data Tech Team
Apr 14, 2022 · Artificial Intelligence

Mastering Time Series Forecasting: From Moving Averages to Transformers

Time series forecasting, essential across weather, finance, and commerce, involves tasks like classification, clustering, anomaly detection, and especially prediction; this article explores its definitions, evaluation metrics, traditional methods, machine‑learning approaches, deep‑learning models such as TFT, and emerging AutoML tools, offering practical insights and best practices.

AutoMLDeep LearningGBDT
0 likes · 27 min read
Mastering Time Series Forecasting: From Moving Averages to Transformers
DataFunTalk
DataFunTalk
Mar 31, 2022 · Artificial Intelligence

Comprehensive Guide to TensorFlow: Modeling, Deployment, and Operations

This article provides an in‑depth overview of the TensorFlow ecosystem, covering Keras modeling productivity tools, classic model examples, AutoKeras and KerasTuner for automated search, data preprocessing pipelines, performance profiling, model optimization, and multiple deployment strategies for servers, browsers, and edge devices.

AutoMLKerasModel Deployment
0 likes · 20 min read
Comprehensive Guide to TensorFlow: Modeling, Deployment, and Operations
Tencent Cloud Developer
Tencent Cloud Developer
Mar 3, 2022 · Artificial Intelligence

Model Distillation for Query-Document Matching: Techniques and Optimizations

We applied knowledge distillation to a video query‑document BERT matcher, compressing the 12‑layer teacher into production‑ready 1‑layer ALBERT and tiny TextCNN students using combined soft, hard, and relevance losses plus AutoML‑tuned hyper‑parameters, achieving sub‑5 ms latency and up to 2.4% AUC improvement over the original model.

ALBERTAutoMLBERT
0 likes · 12 min read
Model Distillation for Query-Document Matching: Techniques and Optimizations
Baobao Algorithm Notes
Baobao Algorithm Notes
Feb 14, 2022 · Artificial Intelligence

Mastering Feature Engineering: From AutoML Dictionaries to Business‑Driven Insights

This article presents a comprehensive, practical methodology for feature engineering that combines brute‑force AutoML‑style dictionary searches, business‑logic‑driven feature creation, and feature‑importance‑guided refinement, illustrating each approach with real Kaggle competition examples and concrete code snippets.

AutoMLKaggledata preprocessing
0 likes · 12 min read
Mastering Feature Engineering: From AutoML Dictionaries to Business‑Driven Insights
Code DAO
Code DAO
Jan 15, 2022 · Artificial Intelligence

How Tuun’s Automated Data Augmentation Boosts AI Model Accuracy

The article explains how Tuun, an open‑source Bayesian‑optimization tool, automatically searches data‑augmentation policies for machine‑learning models, details the setup with Microsoft NNI, provides code and configuration examples, and presents experiments on CIFAR‑10/100 and SVHN showing that Tuun‑generated policies match or surpass expert‑tuned strategies and further improve performance when combined.

AutoMLBayesian OptimizationImage Classification
0 likes · 14 min read
How Tuun’s Automated Data Augmentation Boosts AI Model Accuracy
Meituan Technology Team
Meituan Technology Team
Jan 6, 2022 · Artificial Intelligence

Multi-domain Modeling and AutoML Techniques from Kaggle/KDD Cup Championships

Drawing on seven Kaggle and KDD Cup victories, the article outlines a multi‑domain modeling optimization strategy—covering recommendation, time‑series, and AutoML problems—alongside a three‑module AutoML pipeline and a three‑stage workflow that emphasize systematic evaluation, bias‑variance balance, and robust model‑fusion for competition and industry success.

AutoMLKDD CupKaggle
0 likes · 37 min read
Multi-domain Modeling and AutoML Techniques from Kaggle/KDD Cup Championships
Baobao Algorithm Notes
Baobao Algorithm Notes
Dec 10, 2021 · Artificial Intelligence

AutoX: One-Click Tabular AutoML from Feature Engineering to Model Fusion

AutoX offers a one‑click solution for tabular AutoML by defining feature operators, constructing a searchable feature space, applying efficient feature selection, tuning hyper‑parameters, and performing model ensembling, enabling users with limited ML expertise to automatically generate high‑performing predictive models, as demonstrated on multiple Kaggle datasets.

AutoMLAutoXMachine Learning Automation
0 likes · 7 min read
AutoX: One-Click Tabular AutoML from Feature Engineering to Model Fusion
Kuaishou Tech
Kuaishou Tech
Dec 9, 2021 · Artificial Intelligence

Multi-Task Audio Source Separation (MTASS) and SpeechNAS: AutoML‑Driven Large‑Scale Speaker Recognition

This article presents two ASRU‑2021 accepted works from Kuaishou: MTASS, a multi‑task audio source separation framework that jointly separates speech, music and noise, and SpeechNAS, an AutoML‑based neural architecture search method that achieves state‑of‑the‑art speaker recognition performance with significantly fewer parameters.

AutoMLMTASSNeural Architecture Search
0 likes · 14 min read
Multi-Task Audio Source Separation (MTASS) and SpeechNAS: AutoML‑Driven Large‑Scale Speaker Recognition
21CTO
21CTO
Nov 27, 2021 · Artificial Intelligence

How Huawei’s “Genius Teen” Scaled AutoML to Millions of Phones

Huawei’s 201‑million‑yuan “genius teen” Zhong Zhao leveraged AutoML to deploy high‑precision image‑pixel processing algorithms across tens of millions of Mate and P series smartphones, pioneering large‑scale commercial use of AutoML and advancing mobile visual models with dynamic convolution kernels and adversarial data augmentation.

AutoMLComputer VisionDeep Learning
0 likes · 9 min read
How Huawei’s “Genius Teen” Scaled AutoML to Millions of Phones
ITPUB
ITPUB
Nov 26, 2021 · Artificial Intelligence

How Huawei’s ‘Genius Teen’ Scaled AutoML to Millions of Smartphones

Huawei unveiled the work of young researcher Zhong Zhao, who within a year applied AutoML to pixel‑level image processing on millions of Mate and P series phones, detailing the technical challenges, novel pipeline, performance gains, and his broader contributions to mobile AI research.

AutoMLHuaweiMobile AI
0 likes · 8 min read
How Huawei’s ‘Genius Teen’ Scaled AutoML to Millions of Smartphones
DataFunTalk
DataFunTalk
Nov 20, 2021 · Artificial Intelligence

Intelligent Pre‑Loan Risk Control: Multi‑Loop Feedback Model, AutoML Controller Selection, Unsupervised Feature Extraction, and Metric Design

The article presents Akulaku's intelligent pre‑loan risk control framework, detailing a multi‑loop feedback control model, AutoML‑driven controller selection, unsupervised feature extraction techniques, and a comprehensive metric quantification system to improve stability, steady‑state, and dynamic responses of financial risk management.

AutoMLUnsupervised Learningfeedback control
0 likes · 12 min read
Intelligent Pre‑Loan Risk Control: Multi‑Loop Feedback Model, AutoML Controller Selection, Unsupervised Feature Extraction, and Metric Design
Alimama Tech
Alimama Tech
Nov 10, 2021 · Artificial Intelligence

AutoHERI: Hierarchical Representation Automatic Aggregation for CVR Estimation in Advertising

AutoHERI, a hierarchical representation automatic aggregation model discovered via one‑shot neural architecture search, jointly learns CTR and CVR (and other downstream tasks) to capture cascade relationships, achieving superior AUC and conversion‑rate lifts in large‑scale Alibaba advertising datasets and prompting full production deployment.

AutoMLCVR estimationNeural Architecture Search
0 likes · 15 min read
AutoHERI: Hierarchical Representation Automatic Aggregation for CVR Estimation in Advertising
Ctrip Technology
Ctrip Technology
Sep 16, 2021 · Artificial Intelligence

Automated AI Model Optimization Platform for Travel Services

This article describes the design, automated workflow, functional modules, and performance results of a comprehensive AI model optimization platform built for Ctrip's travel business, covering operator libraries, graph optimization, model compression techniques such as distillation, quantization, pruning, and deployment integration.

AI OptimizationAutoMLInference Acceleration
0 likes · 16 min read
Automated AI Model Optimization Platform for Travel Services
Meituan Technology Team
Meituan Technology Team
Mar 25, 2021 · Artificial Intelligence

Robust Differentiable Architecture Search (DARTS-) for AutoML

The paper introduces DARTS‑, a robust differentiable architecture search method that adds a linearly decaying auxiliary skip‑connection weight to prevent performance collapse, delivering smoother loss landscapes, lower Hessian spikes, and state‑of‑the‑art accuracy on CIFAR‑10, ImageNet and NAS‑Bench‑201, while maintaining efficiency for large‑scale AutoML deployments.

AutoMLDARTSNeural Architecture Search
0 likes · 15 min read
Robust Differentiable Architecture Search (DARTS-) for AutoML
AntTech
AntTech
Oct 22, 2020 · Artificial Intelligence

Ant Group’s Financial AutoML Platform Wins CCF Technology Advancement Excellence Award

Ant Group’s financial intelligent AutoML system received the 2020 CCF Technology Advancement Excellence Award, highlighting its industrial‑grade automated modeling algorithms, high‑performance architecture, and large‑scale deployment that boosted AI modeling efficiency by 50% and risk discrimination by 20% in the finance sector.

AI InnovationAnt GroupAutoML
0 likes · 5 min read
Ant Group’s Financial AutoML Platform Wins CCF Technology Advancement Excellence Award
Tencent Advertising Technology
Tencent Advertising Technology
Sep 22, 2020 · Artificial Intelligence

Automated Machine Learning: Challenges, Techniques, and the SolnML System – Q&A Highlights from the 2020 Tencent Advertising Algorithm Competition Live Series

This article summarizes the Q&A session of the 2020 Tencent Advertising Algorithm Competition live series, covering the fundamentals of automated machine learning, its key technologies, current challenges, and the features and advantages of the SolnML system, while also addressing practical concerns such as hardware support and future research directions.

AIAutoMLMeta Learning
0 likes · 13 min read
Automated Machine Learning: Challenges, Techniques, and the SolnML System – Q&A Highlights from the 2020 Tencent Advertising Algorithm Competition Live Series
Meituan Technology Team
Meituan Technology Team
Aug 27, 2020 · Artificial Intelligence

Automated Graph Representation Learning for KDD Cup 2020 AutoGraph: Technical Solution and Advertising Applications

The team built an automated graph learning framework that preprocesses diverse graphs, employs four GNN architectures, conducts rapid hyper‑parameter tuning, and fuses models with density‑aware weighting, securing first place in KDD Cup 2020 AutoGraph and boosting Meituan’s ad recall and CTR prediction.

AutoMLKDD Cupgraph neural networks
0 likes · 30 min read
Automated Graph Representation Learning for KDD Cup 2020 AutoGraph: Technical Solution and Advertising Applications
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-KnowledgeModel Training
0 likes · 23 min read
Meta‑Knowledge Transfer for Automated Machine Learning: System Architecture and Methodology
DataFunTalk
DataFunTalk
Jul 1, 2020 · Artificial Intelligence

Architecture and Implementation of Autohome's Machine Learning Platform

The article presents a comprehensive overview of Autohome's one‑stop machine learning platform, detailing its background, architecture, resource scheduling, data processing, model training (including distributed deep learning), deployment, real‑world applications such as purchase‑intent and recommendation models, and future development directions.

AutoMLDeep LearningDistributed Training
0 likes · 19 min read
Architecture and Implementation of Autohome's Machine Learning Platform
iQIYI Technical Product Team
iQIYI Technical Product Team
Jun 12, 2020 · Artificial Intelligence

Deepthought: An End‑to‑End Machine Learning Platform at iQIYI

Deepthought is iQIYI’s end‑to‑end machine‑learning platform that unifies distributed frameworks, decouples pipeline stages, integrates with Tongtian Tower, and offers visual drag‑and‑drop configuration, evolving from a fraud‑detection prototype to a generic system with real‑time inference, automated hyper‑parameter optimization, and support for large‑scale data across anti‑fraud, recommendation, and analytics workloads.

AI PlatformAutoMLParameter Server
0 likes · 13 min read
Deepthought: An End‑to‑End Machine Learning Platform at iQIYI
DataFunTalk
DataFunTalk
Jun 8, 2020 · Artificial Intelligence

Augmented Analytics: Concepts, Key Technologies, and Practical Applications

This article explains the concept of augmented analytics, compares it with traditional BI, outlines its impact on data preparation, analysis, and machine learning, and reviews the underlying technologies such as NLQ, NLG, AutoML, and data robots, supported by Gartner insights and industry examples.

AutoMLBusiness Intelligenceaugmented analytics
0 likes · 25 min read
Augmented Analytics: Concepts, Key Technologies, and Practical Applications
DataFunTalk
DataFunTalk
Apr 30, 2020 · Artificial Intelligence

Weight‑Sharing Neural Architecture Search: Challenges, Methods, and Future Directions

This article reviews the major challenges of AI—data, model, and knowledge—explains why automated machine learning and neural architecture search are crucial, analyzes weight‑sharing NAS algorithms and their instability, presents various improved DARTS‑based methods, and discusses experimental results and future research directions.

AIAutoMLDARTS
0 likes · 15 min read
Weight‑Sharing Neural Architecture Search: Challenges, Methods, and Future Directions
JD Tech Talk
JD Tech Talk
Apr 29, 2020 · Artificial Intelligence

Neural Architecture Search: A Survey – Overview, Methods, and Future Directions

This article surveys the field of Neural Architecture Search (NAS), reviewing its motivations, categorizing methods by search space, search strategy, and performance evaluation, summarizing historical approaches, recent advances, and outlining promising future research directions.

AutoMLNAS SurveyNeural Architecture Search
0 likes · 25 min read
Neural Architecture Search: A Survey – Overview, Methods, and Future Directions
JD Tech Talk
JD Tech Talk
Apr 24, 2020 · Artificial Intelligence

Automated Machine Learning System Architecture and Hyper‑Parameter Optimization Process

This article presents a comprehensive automated machine‑learning platform that abstracts task design, hyper‑parameter search space management, optimization engines, algorithm repositories, training/evaluation engines, model repositories and monitoring panels, offering both expert‑assisted and code‑free modes to accelerate model building while reducing reliance on specialist knowledge.

AI PlatformAutoMLModel Management
0 likes · 17 min read
Automated Machine Learning System Architecture and Hyper‑Parameter Optimization Process
Alibaba Cloud Developer
Alibaba Cloud Developer
Apr 2, 2020 · Artificial Intelligence

How AutoML Transformed AR Scanning: Faster, Smaller, More Accurate Models

In 2020, the AR “scan‑for‑fortune” feature achieved a full AutoML rollout on the xNN‑Cloud platform, automating network architecture design and the entire model development pipeline, which cut Android inference time by over 50%, iOS by 30%, reduced model size, and boosted accuracy by 1.6% while handling billions of in‑client inferences.

AR VisionAutoMLMobileNet
0 likes · 15 min read
How AutoML Transformed AR Scanning: Faster, Smaller, More Accurate Models
HomeTech
HomeTech
Mar 18, 2020 · Artificial Intelligence

Automobile Home Recommendation System Architecture and Ranking Models

This article presents a comprehensive overview of the Automobile Home recommendation system, detailing its objectives, architecture, various ranking models from LR to DeepFM, online learning mechanisms, service APIs, feature engineering pipelines, model training platforms, debugging tools, and future optimization directions.

AB testingAutoMLOnline Learning
0 likes · 18 min read
Automobile Home Recommendation System Architecture and Ranking Models
DataFunTalk
DataFunTalk
Feb 7, 2020 · Artificial Intelligence

Automated Machine Learning for Interaction Functions in Collaborative Filtering

This article presents a comprehensive study on using automated machine learning (AutoML) to design interaction functions for collaborative filtering, introducing the SIF framework, detailing its search space, one‑shot algorithm, neural architecture search integration, and demonstrating superior performance on benchmark recommendation datasets.

AutoMLInteraction FunctionNeural Architecture Search
0 likes · 17 min read
Automated Machine Learning for Interaction Functions in Collaborative Filtering
JD Tech Talk
JD Tech Talk
Dec 5, 2019 · Artificial Intelligence

An Overview of Automated Machine Learning (AutoML): Definitions, Algorithms, Frameworks, and Open Challenges

This article provides a comprehensive overview of Automated Machine Learning (AutoML), covering its definition, objectives, research areas, hyperparameter optimization methods, pipeline construction, major CASH algorithms, open-source frameworks such as AutoSklearn and NNI, practical case studies, and current open research challenges.

AutoMLCase StudyNeural Architecture Search
0 likes · 14 min read
An Overview of Automated Machine Learning (AutoML): Definitions, Algorithms, Frameworks, and Open Challenges
DataFunTalk
DataFunTalk
Oct 18, 2019 · Artificial Intelligence

Reinforcement Learning Based Neural Architecture Search: Methods and Advances

This article reviews reinforcement‑learning‑driven neural architecture search, covering layer‑based, block‑based, and connection‑based strategies, as well as advanced techniques such as inverse reinforcement learning, graph hyper‑networks, Monte‑Carlo tree search, and knowledge‑distillation‑based model compression.

AutoMLMonte Carlo Tree SearchNeural Architecture Search
0 likes · 23 min read
Reinforcement Learning Based Neural Architecture Search: Methods and Advances
Tencent Advertising Technology
Tencent Advertising Technology
Oct 17, 2019 · Artificial Intelligence

Visual Algorithm Applications in Advertising Scenarios

The talk outlines how Tencent Advertising leverages deep‑learning visual algorithms—including GCN‑based edge refinement, template generation, AutoML‑driven smart review, and a dual‑tower click‑through‑rate model—to automate creative production, improve ad quality, and enhance user experience across creation, review, and playback stages.

AIAdvertisingAutoML
0 likes · 7 min read
Visual Algorithm Applications in Advertising Scenarios
Huawei Cloud Developer Alliance
Huawei Cloud Developer Alliance
Oct 15, 2019 · Artificial Intelligence

How ModelArts Powers AI Development and Seamless Edge‑Cloud Deployment

This article reviews Huawei's ModelArts platform, detailing its data processing, algorithm development, high‑performance training, edge‑cloud model deployment, auto‑learning capabilities, and real‑world use cases such as invisible payment and intelligent waste classification, while outlining future ecosystem prospects.

AI PlatformAutoMLComputer Vision
0 likes · 14 min read
How ModelArts Powers AI Development and Seamless Edge‑Cloud Deployment
DataFunTalk
DataFunTalk
Sep 24, 2019 · Artificial Intelligence

An Overview of Automated Machine Learning (AutoML): Concepts, Challenges, and Techniques

This article provides a comprehensive overview of AutoML, describing its motivation, formal definition, typical machine‑learning pipeline, key challenges, various optimizer and evaluator strategies—including simple search, heuristic, model‑based, reinforcement learning, and meta‑learning approaches—along with practical applications and future prospects.

AutoMLMeta LearningNeural Architecture Search
0 likes · 12 min read
An Overview of Automated Machine Learning (AutoML): Concepts, Challenges, and Techniques
DataFunTalk
DataFunTalk
Sep 11, 2019 · Artificial Intelligence

AutoML for Tabular Data: Research, Techniques, and Applications

This talk presents the research and practical deployment of AutoML for tabular data, covering background, automated feature engineering and selection, hyper‑parameter optimization, the AutoCross feature‑crossing system, case studies, and future directions, demonstrating its advantages over Google Cloud AutoML on multiple Kaggle competitions.

AutoMLfeature engineeringhyperparameter optimization
0 likes · 14 min read
AutoML for Tabular Data: Research, Techniques, and Applications
DataFunTalk
DataFunTalk
Jun 25, 2019 · Artificial Intelligence

Applying AutoML to Recommendation Systems: Techniques, Optimizations, and Practical Insights

This article presents a comprehensive overview of applying Automated Machine Learning (AutoML) to recommendation systems, detailing methods for data preprocessing, feature engineering, model selection, hyper‑parameter optimization, and neural architecture search, and shares practical experiences and performance gains observed in real‑world deployments.

AutoMLfeature engineeringhyperparameter optimization
0 likes · 28 min read
Applying AutoML to Recommendation Systems: Techniques, Optimizations, and Practical Insights
Alibaba Cloud Developer
Alibaba Cloud Developer
Apr 15, 2019 · Artificial Intelligence

Why Deep Learning Finally Succeeded and What Challenges Lie Ahead

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.

AI ChallengesAI EngineeringAutoML
0 likes · 9 min read
Why Deep Learning Finally Succeeded and What Challenges Lie Ahead
Hulu Beijing
Hulu Beijing
Mar 28, 2019 · Artificial Intelligence

Mastering Bayesian Hyperparameter Optimization: A Practical Guide

This article explains what hyper‑parameters are, why their tuning is a black‑box problem, and how Bayesian optimization—using surrogate models, acquisition functions, and posterior inference—offers a more efficient alternative to grid or random search, while also listing popular open‑source tools and discussing its limitations.

Acquisition FunctionAutoMLBayesian Optimization
0 likes · 8 min read
Mastering Bayesian Hyperparameter Optimization: A Practical Guide
JD Tech Talk
JD Tech Talk
Mar 1, 2019 · Artificial Intelligence

Introduction to H2O AutoML: Overview, Practical Workflow, and Model Deployment

This article introduces the open‑source H2O platform, explains how to install and use its Python API for data loading, preprocessing, model training with GBM and AutoML, evaluates results with AUC, and describes model deployment via POJO/MOJO as well as the visual Flow UI, concluding with reflections on the role of automated modeling in data science.

AutoMLData ScienceH2O
0 likes · 12 min read
Introduction to H2O AutoML: Overview, Practical Workflow, and Model Deployment
Tencent Cloud Developer
Tencent Cloud Developer
Dec 13, 2018 · Artificial Intelligence

Everything you need to know about AutoML and Neural Architecture Search

AutoML and Neural Architecture Search automate deep‑learning model design by using controller networks to explore and evaluate candidate architectures, with efficient variants like PNAS and ENAS reducing cost, while platforms such as Google Cloud AutoML and open‑source AutoKeras make these techniques accessible, promising broader, democratized AI breakthroughs.

AutoMLDeep LearningENAS
0 likes · 7 min read
Everything you need to know about AutoML and Neural Architecture Search
Tencent Cloud Developer
Tencent Cloud Developer
Dec 11, 2018 · Artificial Intelligence

Everything You Need to Know About AutoML and Neural Architecture Search

AutoML and Neural Architecture Search automate deep‑learning model design by sampling and training network blocks, using reinforcement‑learning or efficient weight‑sharing strategies such as PNAS and ENAS, enabling high‑accuracy architectures in days on a single GPU, with services like Google Cloud AutoML and open‑source tools like AutoKeras, while future research aims to expand search spaces beyond hand‑crafted blocks.

AutoMLDeep LearningENAS
0 likes · 9 min read
Everything You Need to Know About AutoML and Neural Architecture Search
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
Youku Technology
Youku Technology
Oct 25, 2018 · Artificial Intelligence

Interview with Wang Xiaobo (Yongshu) on Large‑Scale Machine Learning, Recommendation Systems, and AutoML at Alibaba and Youku

At the AI Pioneer Conference, Wang Xiaobo, head of Alibaba’s Commercial Machine Intelligence and Youku’s algorithm teams, discussed large‑scale distributed learning, recommendation challenges such as cold‑start and video heterogeneity, AutoML innovations, multi‑modal search during promotions, and the future demand for specialists in few‑shot learning and domain adaptation.

AutoMLLarge-Scale Distributed Learningcold start
0 likes · 21 min read
Interview with Wang Xiaobo (Yongshu) on Large‑Scale Machine Learning, Recommendation Systems, and AutoML at Alibaba and Youku
21CTO
21CTO
Jul 26, 2018 · Cloud Computing

Google Cloud Next 18 Highlights: TPU 3.0, AutoML Breakthroughs, and AI Strategy

Google Cloud NEXT 18 in San Francisco unveiled the alpha‑tested Cloud TPU 3.0, major AutoML enhancements, and the Contact Center AI solution, while CEO Diane Greene highlighted AI and security investments and the cloud’s rapid revenue growth, signaling Google’s push to outpace AWS, Azure, and IBM.

AIAutoMLGoogle Cloud
0 likes · 7 min read
Google Cloud Next 18 Highlights: TPU 3.0, AutoML Breakthroughs, and AI Strategy
High Availability Architecture
High Availability Architecture
May 28, 2018 · Artificial Intelligence

Interview with GIAC AI Forum Lecturer Long Mingkang on Building AI Platforms, Speech Recognition Challenges, and Future AI Trends

In this interview, Long Mingkang, Vice President of iFlytek's Cloud Computing Institute, shares his experience building large‑scale speech cloud services, discusses the technical hurdles of speech recognition and AI platform development, compares TensorFlow and MXNet, and offers insights on AutoML, industry trends, and how engineers can master AI.

AIAI PlatformsAutoML
0 likes · 13 min read
Interview with GIAC AI Forum Lecturer Long Mingkang on Building AI Platforms, Speech Recognition Challenges, and Future AI Trends
21CTO
21CTO
Oct 20, 2017 · Artificial Intelligence

Google AutoML Writes Code Faster Than Humans – AI Beats Programmers

Google's AutoML system can automatically generate and improve machine‑learning code, outperforming human researchers with record‑high accuracy on image‑recognition tasks and demonstrating that AI‑driven self‑replicating programs can surpass programmers in just a few hours.

AI code generationAutoMLGoogle AI
0 likes · 3 min read
Google AutoML Writes Code Faster Than Humans – AI Beats Programmers