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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

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 SearchHyperparameter Optimization

0 likes · 10 min read

Tencent's AutoML Research for Advertising Recommendation Systems

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.

AutoMLData VisualizationLibraries

0 likes · 9 min read

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

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

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.

AutoGLAutoMLGraph Neural Networks

0 likes · 19 min read

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

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 PlatformAutoMLAutomation

0 likes · 8 min read

AI Platform Architecture and Automation in Machine Learning

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

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 PlatformAutoMLConference

0 likes · 4 min read

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

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 PAIAutoMLFeature Engineering

0 likes · 15 min read

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

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 deploymentAutoMLData Engineering

0 likes · 20 min read

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

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

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.

AutoMLGraph Neural Networksembedding

0 likes · 21 min read

Automated Knowledge Graph Representation Learning: From Triples to Subgraphs

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.

AutoMLGraph Neural Networksembedding

0 likes · 20 min read

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

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.

AIAutoMLBig Data

0 likes · 13 min read

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

DataFunTalk

Apr 19, 2022 · Artificial Intelligence

Intelligent Risk Control Platform: Design Principles, Strategy and Model Lifecycle Management, and Architecture

This article presents a comprehensive overview of an intelligent risk control platform, covering its design background, six core characteristics, the "five‑full double‑core" concept, end‑to‑end strategy and model lifecycle management, business architecture atomization, and real‑world anti‑fraud case studies.

AIAutoMLData Engineering

0 likes · 13 min read

Intelligent Risk Control Platform: Design Principles, Strategy and Model Lifecycle Management, and Architecture

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.

AutoMLGBDTProphet

0 likes · 27 min read

Mastering Time Series Forecasting: From Moving Averages to Transformers

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

Python Programming Learning Circle

Mar 10, 2022 · Artificial Intelligence

Top 7 Python Libraries and Packages of the Year for Data Science and AI

This article reviews the seven most notable Python libraries and packages of 2018 for data scientists and AI practitioners, including AdaNet, TPOT, SHAP, Optimus, spaCy, Jupytext, and Chartify, with descriptions, installation commands, and usage examples.

AutoMLLibrariesNLP

0 likes · 15 min read

Top 7 Python Libraries and Packages of the Year for Data Science and AI

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