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Ray's Galactic Tech
Ray's Galactic Tech
Oct 1, 2026 · Operations

Beyond v2_final: Production-Grade ML Data Versioning & Experiment Tracking with DVC + MLflow

This article demonstrates a complete, reproducible ML pipeline using DVC for data versioning and MLflow for experiment tracking, covering immutable data snapshots, time-based splits, pipeline definitions, model registration gates, canary deployment, rollback procedures, CI integration, and retention policies — all validated with concrete code and failure-mode analysis.

CI/CDDVCMLOps
0 likes · 60 min read
Beyond v2_final: Production-Grade ML Data Versioning & Experiment Tracking with DVC + MLflow
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Aug 23, 2026 · Artificial Intelligence

Why AI Projects Look Great but Perform Poorly? A Practitioner’s Deep Retrospective

The article analyzes why AI projects that shine in proof‑of‑concepts often falter in production, highlighting four core challenges—probabilistic uncertainty, data quality, engineering complexity, and misleading accuracy metrics—and proposes four practical ways to break through these obstacles.

AI DeploymentMLOpsdata governance
0 likes · 13 min read
Why AI Projects Look Great but Perform Poorly? A Practitioner’s Deep Retrospective
ITPUB
ITPUB
Aug 22, 2026 · Databases

How DBAs Can Transform with AI: Insights from DTCC 2026

The 17th China Database Technology Conference showcased DBA‑AI transformation strategies, highlighting five‑stage evolution paths, lightweight big‑data + AI architectures, and the emerging Agentic Data Stack, while warning of AI hallucination risks and emphasizing human‑AI collaboration for future data engineering.

AIAgentic AIDBA
0 likes · 12 min read
How DBAs Can Transform with AI: Insights from DTCC 2026
CTO Full-Stack Academy
CTO Full-Stack Academy
Aug 22, 2026 · Industry Insights

A Complete Blueprint for Implementing Enterprise Smartization

The article provides a detailed, step‑by‑step framework for transforming a traditional enterprise into a fully integrated smart organization, covering core definitions, four key capabilities, a five‑stage rollout plan, common pitfalls with remedies, and a real‑world case study that quantifies the resulting efficiency, cost, and energy gains.

Data IntegrationMLOpscase study
0 likes · 18 min read
A Complete Blueprint for Implementing Enterprise Smartization
Lakehouse Research Base
Lakehouse Research Base
Aug 20, 2026 · Big Data

Big Data Across 10 Industries: Finance, New Energy & AI — Who Reigns as Data King?

This article maps big data applications across ten industries — finance, retail, manufacturing, healthcare, government, transportation, new energy, AI, agriculture, and property — detailing use cases, technical architectures, and measurable value, concluding that AI and new energy form a mutually reinforcing data-driven loop.

Artificial IntelligenceMLOpsReal-time Analytics
0 likes · 32 min read
Big Data Across 10 Industries: Finance, New Energy & AI — Who Reigns as Data King?
TechVision Expert Circle
TechVision Expert Circle
Aug 12, 2026 · Industry Insights

How CIOs Can Navigate the EU AI Act’s New Compliance Minefields

The EU AI Act entered full enforcement in August 2026, imposing fines up to 7% of global revenue and demanding transparent AI documentation, risk‑based classification, and mandatory content labeling, prompting CIOs to adopt proactive, architecture‑level compliance measures to avoid costly penalties.

AI ActAI GovernanceCIO
0 likes · 12 min read
How CIOs Can Navigate the EU AI Act’s New Compliance Minefields
Yunqi AI+
Yunqi AI+
Aug 2, 2026 · Operations

How to Scale Operations When AI Agents Multiply

The article analyzes why traditional hand‑over models fail as the number of AI agents grows, proposes a shared‑semantic and result‑driven management approach inspired by Salesforce and Palantir, and outlines a three‑layer organizational model with dual ownership to keep a digital workforce sustainable.

AI operationsAgentOpsDigital Workforce
0 likes · 25 min read
How to Scale Operations When AI Agents Multiply
Subtle Storm
Subtle Storm
Jun 19, 2026 · Artificial Intelligence

AI Concepts Every Architect Must Master

The article outlines the essential AI fundamentals architects need—from basic machine‑learning principles, token limits, and learning paradigms to RAG pipelines, vector‑database choices, AI agents, prompt engineering, and MLOps practices—so they can design reliable, scalable AI‑driven systems.

AIAI agentsMLOps
0 likes · 7 min read
AI Concepts Every Architect Must Master
Subtle Storm
Subtle Storm
May 31, 2026 · Artificial Intelligence

Essential AI Knowledge Every Top Architect Must Master

The article outlines the AI topics that modern architects need to master—including fundamentals, weak and narrow AI, generative models, large language models, Transformers, prompt engineering, multimodal concepts, intelligent agents, end‑to‑end system design, MLOps, distributed high‑performance computing, and technology‑cost trade‑offs—highlighting why AI expertise is now a core requirement for architectural roles.

AIDistributed ComputingMLOps
0 likes · 5 min read
Essential AI Knowledge Every Top Architect Must Master
Digital Planet
Digital Planet
May 29, 2026 · Industry Insights

5 Essential Skills Data Professionals Must Master in 2026

In the AI‑driven era of 2026, data professionals need to focus on five high‑impact capabilities—data governance, practical large‑model usage, MLOps, data storytelling, and AI compliance—to stay indispensable, with each skill backed by industry reports, job growth data, and concrete learning pathways.

2026 trendsAI SkillsAI compliance
0 likes · 13 min read
5 Essential Skills Data Professionals Must Master in 2026
Woodpecker Software Testing
Woodpecker Software Testing
Apr 15, 2026 · Artificial Intelligence

How AI Testing Tools Redefine Performance Optimization: A New Paradigm

Amid exploding large‑model deployments, AI teams struggle with slow test feedback, but AI‑native testing tools—through intelligent load modeling, inference‑layer root‑cause analysis, and self‑healing loops—demonstrate concrete latency reductions, resource savings, and faster issue remediation.

AI testingMLOpsPerformance Optimization
0 likes · 6 min read
How AI Testing Tools Redefine Performance Optimization: A New Paradigm
Woodpecker Software Testing
Woodpecker Software Testing
Apr 10, 2026 · Artificial Intelligence

2026 Model Evaluation Reaches the Cost‑Benefit Threshold

In 2026, model evaluation has become the pivotal bottleneck in AI engineering, with exploding compute, data‑compliance, and tooling costs forcing a shift from labor‑intensive testing to quantifiable business value, and three levers—dynamic granularity, synthetic data loops, and evaluation‑as‑a‑service—offering a path to a cost‑benefit inflection point.

AI complianceDynamic GranularityMLOps
0 likes · 7 min read
2026 Model Evaluation Reaches the Cost‑Benefit Threshold
Woodpecker Software Testing
Woodpecker Software Testing
Apr 3, 2026 · Artificial Intelligence

Why 80% of AI Projects Fail: Bridging Model Evaluation from Theory to Real‑World Impact

The article explains that most AI project failures stem from unrealistic evaluation rather than model intelligence, and outlines concrete practices—business‑aligned metrics, scenario sandboxes, human‑in‑the‑loop reviews, and auditable documentation—to make model evaluation truly actionable.

AI DeploymentAI reliabilityBusiness Metrics
0 likes · 7 min read
Why 80% of AI Projects Fail: Bridging Model Evaluation from Theory to Real‑World Impact
AI Info Trend
AI Info Trend
Mar 10, 2026 · Industry Insights

Southeast Asia’s AI Surge: Opportunities, Challenges, and the 2026 Roadmap

McKinsey’s report reveals that AI is moving from pilot projects to large‑scale deployment across Southeast Asia, driven by youthful, mobile‑first populations and massive cloud investments, yet talent shortages, integration complexity, and unclear ROI remain the biggest hurdles for enterprises.

2026 StrategyAIAgentic AI
0 likes · 7 min read
Southeast Asia’s AI Surge: Opportunities, Challenges, and the 2026 Roadmap
Woodpecker Software Testing
Woodpecker Software Testing
Mar 6, 2026 · Artificial Intelligence

How RAG Testing Teams Can Successfully Transform in 2024

With RAG becoming the backbone of enterprise AI, traditional API‑UI testing misses critical semantic errors, leading to high hallucination rates; this article outlines why conventional methods fail and presents a three‑pillar transformation—skill rebuilding, process reengineering, and advanced tooling—backed by real‑world case studies.

AI testingLLMMLOps
0 likes · 9 min read
How RAG Testing Teams Can Successfully Transform in 2024
Woodpecker Software Testing
Woodpecker Software Testing
Mar 1, 2026 · Artificial Intelligence

Automating Regression Tests for TensorRT Inference Services

The article outlines a comprehensive, repeatable regression testing framework for TensorRT inference pipelines, covering engine build validation, functional correctness against golden outputs, performance monitoring, common pitfalls, and CI/CD integration to ensure model updates remain both fast and reliable.

INT8 quantizationMLOpsPerformance Regression
0 likes · 12 min read
Automating Regression Tests for TensorRT Inference Services
Yunqi AI+
Yunqi AI+
Feb 13, 2026 · Artificial Intelligence

Key Challenges When Enterprises Deploy AI-in-the-Loop

The article outlines a four‑layer framework—process, technology, risk, and culture—to help enterprises implement AI‑in‑the‑loop safely, ensuring AI assists decisions while humans retain final authority, with concrete governance, data, and organizational practices.

AI-in-the-loopMLOpsRisk Governance
0 likes · 7 min read
Key Challenges When Enterprises Deploy AI-in-the-Loop
Thought Artisan
Thought Artisan
Jan 3, 2026 · Artificial Intelligence

SE4AI: What Makes AI Software Engineering Different?

The article distinguishes general software engineering practices — observability, security, reliability — from AI-specific challenges like LLM uncertainty, safety guardrails, agent frameworks, memory engineering, and MLOps, referencing Ant Group's open-source LLM landscape.

AI SystemsAgent FrameworksAnt Group
0 likes · 3 min read
SE4AI: What Makes AI Software Engineering Different?
21CTO
21CTO
Oct 6, 2025 · Artificial Intelligence

How to Become an AI Engineer: Skills, Workflow, and Career Path

This guide explains what AI engineering entails, outlines the end‑to‑end workflow from problem definition and data preparation through model development, deployment, and monitoring, and highlights the essential programming, cloud, and MLOps skills, career tracks, emerging trends, and salary outlook for aspiring AI engineers.

AI EngineeringCloud ComputingMLOps
0 likes · 11 min read
How to Become an AI Engineer: Skills, Workflow, and Career Path
DevOps Cloud Academy
DevOps Cloud Academy
Sep 25, 2025 · Artificial Intelligence

How to Build Scalable MLOps Infrastructure for Enterprise AI Success

This article explains what MLOps is, why a robust MLOps framework is essential for businesses, outlines its core components, compares MLOps with AIOps, details the benefits of investing in MLOps, and provides a step‑by‑step guide to designing enterprise‑grade AI MLOps infrastructure.

AI infrastructureMLOpsMachine Learning Operations
0 likes · 17 min read
How to Build Scalable MLOps Infrastructure for Enterprise AI Success
Alibaba Cloud Observability
Alibaba Cloud Observability
Jun 16, 2025 · Artificial Intelligence

Mastering AI Application Observability: From Metrics to Full‑Stack Tracing

This article explains why cost and performance are critical in the AI era, outlines the three main pain points of AI application development, and details a full‑stack observability solution—including architecture layers, key metrics like TTFT and TPOT, OpenTelemetry tracing, and practical tips for frameworks such as Dify—integrated into Alibaba Cloud CloudMonitor 2.0.

AI ObservabilityAI application monitoringLLM performance
0 likes · 21 min read
Mastering AI Application Observability: From Metrics to Full‑Stack Tracing
Architect's Alchemy Furnace
Architect's Alchemy Furnace
Jun 4, 2025 · Artificial Intelligence

What Is an AI Engineer? Roles, Skills, and the Future of LLM‑Powered Systems

This article examines the evolving role of the AI engineer, contrasting it with AI researchers, ML engineers, and software engineers, outlines essential skills such as prompt engineering, MLOps, and data integration, and predicts how AI engineering will become a pivotal, high‑demand discipline in the coming years.

AI EngineeringAI SystemsAgentic RAG
0 likes · 17 min read
What Is an AI Engineer? Roles, Skills, and the Future of LLM‑Powered Systems
Lakehouse Research Base
Lakehouse Research Base
May 22, 2025 · Big Data

Four Core Data Science Challenges and the DataOps Solution

The article identifies four core challenges in data science—data acquisition and management, technical engineering gaps, team collaboration barriers, and maintenance issues—and proposes a DataOps approach across technology, process, and organization to transform data assets into business value.

Data QualityData ScienceDataOps
0 likes · 9 min read
Four Core Data Science Challenges and the DataOps Solution
Alibaba Cloud Developer
Alibaba Cloud Developer
May 14, 2025 · Artificial Intelligence

Deploy Alibaba’s Qwen3 LLM in 10 Minutes with Bailei Platform

Learn how to quickly set up Alibaba Cloud’s Bailei platform to call the open-source Qwen3 large language model, explore its cost‑effective performance, dual‑mode reasoning, multilingual support, and enhanced agent capabilities, and follow step‑by‑step instructions for API key configuration, Cherry Studio integration, and tool‑calling setup.

AI DeploymentAlibaba CloudMLOps
0 likes · 6 min read
Deploy Alibaba’s Qwen3 LLM in 10 Minutes with Bailei Platform
DeWu Technology
DeWu Technology
May 9, 2025 · Artificial Intelligence

Growth Story of a Technical Lead: Building a One‑Stop Large‑Model Training and Inference Platform at Dewu

Meng, a former Tencent and Alibaba engineer, led Dewu’s one‑stop large‑model training and inference platform, cutting integration costs, creating a shared GPU pool and CI/CD pipeline, building a Milvus vector‑database, and driving self‑directed learning that boosted business value, user experience, and set a roadmap for future RAG and cloud‑native optimizations.

AI platformMLOpscareer development
0 likes · 18 min read
Growth Story of a Technical Lead: Building a One‑Stop Large‑Model Training and Inference Platform at Dewu
Ops Development & AI Practice
Ops Development & AI Practice
Apr 19, 2025 · Industry Insights

Is AI Splitting into Two Worlds? Building Models vs Building Apps

The article analyzes the emerging divide in the AI ecosystem where a few giants focus on resource‑intensive large‑model research and training while most companies shift to leveraging existing models for application development, outlining the implications, challenges, and strategic advice for developers and enterprises.

AIApplication DevelopmentMLOps
0 likes · 10 min read
Is AI Splitting into Two Worlds? Building Models vs Building Apps
Bitu Technology
Bitu Technology
Mar 21, 2025 · Backend Development

Optimizing Redis Latency for an Online Feature Store: A Batch Query Case Study

This article describes how Tubi improved the latency of its Redis‑backed online feature store for machine‑learning inference by analyzing query patterns, measuring client‑side bottlenecks, and applying optimizations such as binary Avro encoding, MGET usage, virtual partitioning, and parallel deserialization to meet a sub‑10 ms SLA.

Feature StoreMLOpsPerformance Optimization
0 likes · 9 min read
Optimizing Redis Latency for an Online Feature Store: A Batch Query Case Study
DataFunSummit
DataFunSummit
Jan 11, 2025 · Artificial Intelligence

Generative AI Applications, MLOps, and LLMOps: A Comprehensive Overview

This article presents a detailed overview of generative AI lifecycle management, covering practical use cases such as email summarization, the roles of providers, fine‑tuners and consumers, MLOps/LLMOps processes, retrieval‑augmented generation, efficient fine‑tuning methods like PEFT, and Amazon Bedrock services for model deployment and monitoring.

Amazon BedrockLLMOpsMLOps
0 likes · 14 min read
Generative AI Applications, MLOps, and LLMOps: A Comprehensive Overview
DeWu Technology
DeWu Technology
Dec 11, 2024 · Artificial Intelligence

MLOps Practices for Improving Order Fulfillment Timeliness

The supply‑chain team leveraged core MLOps practices—versioning, testing, automated reproducible pipelines, deployment monitoring, and documentation—to eliminate data leakage, ensure online consistency, and accelerate model upgrades, using traffic‑replay, FAAS‑based decoupling, and approval workflows, ultimately cutting order‑fulfillment times, reducing costs, and enabling business teams to adopt reliable AI models at scale.

MLOpsautomationdata versioning
0 likes · 18 min read
MLOps Practices for Improving Order Fulfillment Timeliness
Baidu Geek Talk
Baidu Geek Talk
Oct 30, 2024 · Cloud Computing

Baidu Cloud Infrastructure for AI-Native Era

Baidu Intelligent Cloud outlines how its evolving, high-performance infrastructure—featuring rapid 3-minute instance provisioning, over 200 GB bandwidth, elastic computing, specialized storage, and AI-driven MLOps tools—enables AI-native model training and deployment across booming sectors such as automotive and finance, supporting the industry’s shift to AI-centric cloud services.

Case StudiesCloud ComputingMLOps
0 likes · 9 min read
Baidu Cloud Infrastructure for AI-Native Era
Baidu Intelligent Cloud Tech Hub
Baidu Intelligent Cloud Tech Hub
Oct 28, 2024 · Cloud Native

How Baidu Smart Cloud Reinvents Cloud‑Native Infrastructure for the AI‑Native Era

The talk outlines Baidu Smart Cloud's comprehensive cloud‑native redesign—including ultra‑elastic compute, AI‑focused storage, high‑performance networking, AI‑driven operations, and edge‑distributed services—illustrated with automotive and fintech case studies that demonstrate how enterprises can accelerate digital transformation in the AI‑native age.

AI infrastructureData LakeMLOps
0 likes · 12 min read
How Baidu Smart Cloud Reinvents Cloud‑Native Infrastructure for the AI‑Native Era
DataFunTalk
DataFunTalk
Jun 11, 2024 · Artificial Intelligence

Intelligent Risk Control: Concepts, Challenges, and Integrated Operational Architecture for Banking

This article explores the concept of intelligent risk control in banking, detailing its AI‑driven architecture, current challenges such as external data costs and model‑deployment friction, and proposes an integrated operational framework that leverages big data, knowledge graphs, and MLOps to enhance risk detection and decision‑making.

Artificial IntelligenceMLOpsbanking
0 likes · 14 min read
Intelligent Risk Control: Concepts, Challenges, and Integrated Operational Architecture for Banking
21CTO
21CTO
Jun 7, 2024 · Artificial Intelligence

10 Essential Tools for Building a Modern AI Data Lake Architecture

This article outlines ten critical components of a modern data lake reference architecture for AI/ML, detailing each function, the supporting vendor tools and open‑source libraries, and how they enable scalable storage, MLOps, distributed training, model hubs, vector search, and data visualization.

AIData LakeDistributed Training
0 likes · 14 min read
10 Essential Tools for Building a Modern AI Data Lake Architecture
DataFunTalk
DataFunTalk
Jun 4, 2024 · Artificial Intelligence

Building an Integrated Intelligent Risk Control System for Banking

The article explores the concept, challenges, and future directions of intelligent banking risk control, emphasizing data integration, AI-driven modeling, feature engineering, MLOps, knowledge graphs, and large‑model applications to create a unified, automated risk management platform.

AIMLOpsbanking
0 likes · 10 min read
Building an Integrated Intelligent Risk Control System for Banking
Architects' Tech Alliance
Architects' Tech Alliance
May 24, 2024 · Industry Insights

What Drives AI's Future? A Four‑Layer Industry Framework Explained

This article breaks down the AI ecosystem into four layers—AI hardware and cloud services, model and algorithm advances, MLOps middleware, and B2B/B2C applications—highlighting how hardware cost reductions, cloud integration, model breakthroughs, and middleware providers shape the market and adoption speed.

AIAI applicationsInfrastructure
0 likes · 6 min read
What Drives AI's Future? A Four‑Layer Industry Framework Explained
DevOps
DevOps
Mar 3, 2024 · Operations

How Generative AI is Transforming DevOps: Benefits, Challenges, and Best Practices

Since 2022, generative AI has become a pervasive trend, and this article explores its integration into DevOps, outlining the technology’s advantages, limitations, emerging trends, and best practices while highlighting how AI‑driven automation reshapes software engineering workflows.

AI ethicsArtificial IntelligenceGenAI
0 likes · 10 min read
How Generative AI is Transforming DevOps: Benefits, Challenges, and Best Practices
Didi Tech
Didi Tech
Jan 25, 2024 · Artificial Intelligence

Ray-native XGBoost Training Platform: Architecture, Performance, and Technical Challenges

Didi’s new Ray‑native XGBoost training platform replaces the fault‑prone Spark solution with a fully Pythonic, fault‑tolerant architecture that leverages Ray’s autoscaling and gang‑scheduling, delivering 2–6× speedups, reduced failure rates, efficient sparse‑vector handling, scalable hyper‑parameter search, and improved resource utilization for large‑scale machine‑learning workloads.

MLOpsRayXGBoost
0 likes · 20 min read
Ray-native XGBoost Training Platform: Architecture, Performance, and Technical Challenges
Baidu Geek Talk
Baidu Geek Talk
Dec 6, 2023 · Industry Insights

From MLOps to LMOps: Challenges and Solutions for Large‑Model Operations

This article reviews the evolution from MLOps to LMOps, outlines the core concepts, challenges, and key technologies such as large‑model inference optimization, prompt engineering, and context‑length extension, and offers a forward‑looking perspective on the future of AI operations.

AI operationsLMOpsMLOps
0 likes · 23 min read
From MLOps to LMOps: Challenges and Solutions for Large‑Model Operations
DataFunSummit
DataFunSummit
Oct 7, 2023 · Artificial Intelligence

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.

AI EngineeringMLOpsnetwork intelligence
0 likes · 20 min read
MLOps Implementation in Network Intelligence: Jiutian Platform Overview
Baidu Intelligent Cloud Tech Hub
Baidu Intelligent Cloud Tech Hub
Aug 8, 2023 · Artificial Intelligence

Unlocking LMOps: How Enterprises Can Master Large Model Operations

This article explains the evolution from traditional machine learning to the current large‑model era, introduces LMOps concepts and key technologies, compares them with MLOps, and showcases Baidu Cloud's Qianfan platform as a practical solution for building, deploying, and managing large language models in industry.

AI operationsBaidu CloudLMOps
0 likes · 22 min read
Unlocking LMOps: How Enterprises Can Master Large Model Operations
Cloud Native Technology Community
Cloud Native Technology Community
Jun 28, 2023 · Artificial Intelligence

Building and Deploying Custom Large Language Models with Alauda Cloud‑Native MLOps

This article explains how enterprises can use the Alauda MLOps platform to quickly set up, fine‑tune, and deploy private large language models on cloud‑native infrastructure, covering notebook preparation, GPU allocation, model download, inference service creation, distributed training pipelines, and Docker image building.

AIFine-tuningMLOps
0 likes · 9 min read
Building and Deploying Custom Large Language Models with Alauda Cloud‑Native MLOps
DataFunSummit
DataFunSummit
Jun 24, 2023 · Artificial Intelligence

From Model to Service: Alibaba Cloud Machine Learning PAI One‑Stop Model Development and Deployment Practice

This article presents an end‑to‑end overview of Alibaba Cloud’s Machine Learning PAI platform, detailing the three‑stage ML workflow, challenges in model development, the role of pre‑trained and open‑source models, PAI’s architecture, a hands‑on demo, and MLOps best practices for efficient model deployment.

Alibaba CloudMLOpsPretrained Models
0 likes · 11 min read
From Model to Service: Alibaba Cloud Machine Learning PAI One‑Stop Model Development and Deployment Practice
DataFunSummit
DataFunSummit
Mar 30, 2023 · Artificial Intelligence

MindAlpha: A High‑Performance Distributed Machine Learning Platform for Advertising

The article introduces MindAlpha, a high‑performance distributed machine‑learning platform built for large‑scale, sparse ad‑tech workloads, detailing its architecture, MLOps pipeline, Spark integration, sync/async training strategies, CPU/GPU choices, model‑splitting techniques, and future directions such as model pruning and AutoML.

AIAd TechMLOps
0 likes · 10 min read
MindAlpha: A High‑Performance Distributed Machine Learning Platform for Advertising
Tencent Advertising Technology
Tencent Advertising Technology
Mar 30, 2023 · Artificial Intelligence

Tencent's Taiji Machine Learning Platform: End-to-End MLOps for Advertising

Tencent’s Taiji machine learning platform, a cloud‑native, distributed parameter‑server system, provides end‑to‑end MLOps for advertising by integrating data ingestion, feature engineering, model training, evaluation, deployment, and monitoring, supporting massive models up to billions of parameters while improving efficiency, scalability, and resource management.

Distributed TrainingMLOpsMachine Learning Platform
0 likes · 18 min read
Tencent's Taiji Machine Learning Platform: End-to-End MLOps for Advertising
DataFunTalk
DataFunTalk
Mar 25, 2023 · Artificial Intelligence

ZhongAn Financial Real‑Time Feature Platform: MLOps Practices, Architecture and Anti‑Fraud Applications

This article presents ZhongAn Financial’s end‑to‑end MLOps workflow and real‑time feature platform architecture, detailing team roles, data pipelines, Flink‑based processing, TableStore storage, anti‑fraud feature design, and answers to common implementation questions, offering a comprehensive guide for building scalable, low‑latency ML services in finance.

FlinkMLOpsTablestore
0 likes · 25 min read
ZhongAn Financial Real‑Time Feature Platform: MLOps Practices, Architecture and Anti‑Fraud Applications
Smart Era Software Development
Smart Era Software Development
Mar 16, 2023 · Artificial Intelligence

10 Essential Elements of Machine Learning System Architecture

The article outlines ten core components—data and feature pipelines, feature store, training and retraining pipelines, metadata store, serving infrastructure, production monitoring, reusable ML pipelines, workflow orchestration, CI/CT/CD, and end‑to‑end quality control—that together form a scalable, reliable architecture for modern machine‑learning systems.

MLOpsfeature engineeringmachine learning
0 likes · 7 min read
10 Essential Elements of Machine Learning System Architecture
DataFunSummit
DataFunSummit
Feb 21, 2023 · Artificial Intelligence

Practices and Reflections on Building an AI Platform at Zhongyuan Bank

This article details Zhongyuan Bank's AI platform construction, covering its objectives, MLOps-driven design, core modules such as data ingestion, processing, model development, training, evaluation, deployment, monitoring, as well as resource orchestration with Kubernetes and Docker, and the accompanying ModelOps governance framework.

AICloud ComputingMLOps
0 likes · 22 min read
Practices and Reflections on Building an AI Platform at Zhongyuan Bank
Efficient Ops
Efficient Ops
Jan 16, 2023 · Artificial Intelligence

How MLOps Is Transforming AI Production in China: Trends, Tools, and Standards

This report examines how MLOps is accelerating AI production in China, highlighting industry adoption across sectors, the booming tool ecosystem, the rise of feature platforms, enhanced observability, performance needs for large models, AI asset management, and the emerging national standards and evaluation results.

AI EngineeringAI standardsFeatureOps
0 likes · 8 min read
How MLOps Is Transforming AI Production in China: Trends, Tools, and Standards
GuanYuan Data Tech Team
GuanYuan Data Tech Team
Dec 29, 2022 · Artificial Intelligence

How AI Transforms DTC Supply‑Chain Replenishment: From Safety‑Stock Theory to Real‑World Deployment

This article explains how AI‑driven forecasting, joint error‑distribution safety‑stock calculations, and MLOps‑backed simulation are combined to optimize DTC replenishment, improve inventory days and stock‑out rates, and address practical deployment challenges in fast‑growing e‑commerce supply chains.

AIMLOpsSimulation
0 likes · 21 min read
How AI Transforms DTC Supply‑Chain Replenishment: From Safety‑Stock Theory to Real‑World Deployment
AntTech
AntTech
Dec 26, 2022 · Artificial Intelligence

AntSec MLOps: Building a Scalable, Automated, and Trustworthy AI Risk‑Control Platform

This article describes the challenges, overall architecture, data development, model monitoring, continuous training, security‑trustworthiness, and future roadmap of Ant Security's intelligent risk‑control platform, illustrating how AI, big data, and cloud computing are integrated to create a scalable, automated MLOps solution for dynamic fraud detection and mitigation.

AIMLOpsautomation
0 likes · 28 min read
AntSec MLOps: Building a Scalable, Automated, and Trustworthy AI Risk‑Control Platform
GuanYuan Data Tech Team
GuanYuan Data Tech Team
Dec 1, 2022 · Artificial Intelligence

Why MLOps Is the Key to Scalable AI Projects

This article explains the concept, significance, and practical case studies of MLOps—showing how integrating DevOps principles with data and machine learning creates reliable, automated pipelines for data quality, model monitoring, error analysis, and continuous integration, ultimately accelerating AI delivery.

AI EngineeringMLOpsMachine Learning Operations
0 likes · 15 min read
Why MLOps Is the Key to Scalable AI Projects
Laiye Technology Team
Laiye Technology Team
Nov 23, 2022 · Artificial Intelligence

Design and Practices of a Data‑Driven OCR Testing System

The article describes Laiye's shift to a data‑driven deep‑learning workflow and presents the design, macro‑ and micro‑analysis features, visual diff tools, distributed tracing, and code examples of their OCR testing system that accelerate model evaluation and iterative optimization.

AIData‑DrivenMLOps
0 likes · 11 min read
Design and Practices of a Data‑Driven OCR Testing System
Efficient Ops
Efficient Ops
Nov 7, 2022 · Artificial Intelligence

Unlocking AI Project Success with the New MLOps Maturity Assessment

This article outlines the background, standards, evaluation items, process, and registration details of a newly launched MLOps development management maturity assessment designed to accelerate AI model delivery and improve operational efficiency across teams.

AI operationsMLOpsmodel deployment
0 likes · 6 min read
Unlocking AI Project Success with the New MLOps Maturity Assessment
DataFunTalk
DataFunTalk
Oct 27, 2022 · Artificial Intelligence

Data‑Centric AI and MLOps: A Case Study of Smart‑Cabin Applications in the Automotive Industry

The talk by Magic Data’s founder Zhang Qingqing outlines the shift from model‑centric to data‑centric AI, introduces Data‑Centric MLOps methodology, and demonstrates its automotive smart‑cabin application, highlighting data quality requirements, collaborative workflow, and performance gains across speech, live‑social and navigation scenarios.

AI applicationsAutomotive AIData-Centric AI
0 likes · 9 min read
Data‑Centric AI and MLOps: A Case Study of Smart‑Cabin Applications in the Automotive Industry
Efficient Ops
Efficient Ops
Oct 26, 2022 · Artificial Intelligence

Unveiling China’s AI Model Delivery Standard: Boosting MLOps and AI Engineering

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.

AIAI EngineeringChina
0 likes · 5 min read
Unveiling China’s AI Model Delivery Standard: Boosting MLOps and AI Engineering
vivo Internet Technology
vivo Internet Technology
Oct 9, 2022 · Artificial Intelligence

vivo Machine Learning Platform: Architecture Design and Practice

vivo’s machine‑learning platform, built for its massive app‑store and e‑commerce ecosystem, streamlines data processing, model training, and deployment through quota‑based resource management, a custom ultra‑large‑scale TensorFlow‑vlps framework, OpenAPI‑driven training, and Jupyter‑integrated interactive development, boosting efficiency for billions of samples and features.

Distributed TrainingMLOpsMachine Learning Platform
0 likes · 12 min read
vivo Machine Learning Platform: Architecture Design and Practice
Efficient Ops
Efficient Ops
Aug 9, 2022 · Operations

How ICBC Accelerated Digital Transformation with XOps: From DevOps to MLOps

ICBC’s software development center outlines its multi‑year journey adopting XOps practices—DevOps, DevSecOps, DataOps, MLOps, AIOps, ChatOps and BizDevOps—to boost development efficiency, enhance security, accelerate data‑driven AI, and cut costs, showcasing measurable improvements in release frequency, defect rates, and operational automation.

AIOpsDataOpsDevOps
0 likes · 13 min read
How ICBC Accelerated Digital Transformation with XOps: From DevOps to MLOps
Efficient Ops
Efficient Ops
Aug 2, 2022 · Artificial Intelligence

How MLOps Boosted AI Service Delivery at China Agricultural Bank

In a detailed interview, the Agricultural Bank of China's R&D center explains how its AI service platform achieved a Level‑3 leading rating in the national MLOps maturity assessment, and how MLOps practices have accelerated model development, improved quality, reduced risk, and driven scalable AI adoption across financial services.

MLOpsModelOpsfinancial AI
0 likes · 10 min read
How MLOps Boosted AI Service Delivery at China Agricultural Bank
NetEase Cloud Music Tech Team
NetEase Cloud Music Tech Team
Jul 6, 2022 · Industry Insights

Inside NetEase Cloud Music’s MLOps: Scaling AI with VK, ECI, and Ceph

This article details NetEase Cloud Music’s four‑layer machine‑learning platform architecture, covering resource provisioning with Visual Kubelet and Alibaba Cloud ECI, Ceph storage optimizations, TensorFlow migration, large‑scale graph neural network support, and end‑to‑end workflow tooling that together enable efficient, cost‑effective AI development and deployment.

CephGPUGraph Neural Network
0 likes · 24 min read
Inside NetEase Cloud Music’s MLOps: Scaling AI with VK, ECI, and Ceph
DataFunSummit
DataFunSummit
Jun 30, 2022 · Artificial Intelligence

MLOps Practices on the Beike Inference Platform: Architecture, Evolution, and Future Plans

This article presents a comprehensive overview of Beike's machine learning platform and its inference service, detailing the platform's architecture, GPU virtualization, cloud‑native migration, MLOps implementation, and future roadmap to achieve cost‑effective, automated AI model deployment at scale.

AIGPU virtualizationInference Platform
0 likes · 13 min read
MLOps Practices on the Beike Inference Platform: Architecture, Evolution, and Future Plans
NetEase Cloud Music Tech Team
NetEase Cloud Music Tech Team
Jun 29, 2022 · Artificial Intelligence

Music FeatureBox: A Custom Feature Store for Machine Learning at NetEase Cloud Music

Music FeatureBox is NetEase Cloud Music’s custom feature store that centralizes metadata, unifies offline and online feature storage across multiple engines, provides a cross‑language DSL for extraction, ensures training‑inference data consistency, and offers built‑in monitoring, thereby streamlining feature engineering and accelerating the platform’s machine‑learning lifecycle.

DataHubFeature StoreMLOps
0 likes · 17 min read
Music FeatureBox: A Custom Feature Store for Machine Learning at NetEase Cloud Music
Efficient Ops
Efficient Ops
Jun 12, 2022 · Artificial Intelligence

Unlocking AI Success: A Deep Dive into the Model/MLOps Capability Maturity Framework

This article explains the globally first AI model development management standard—Model/MLOps Capability Maturity Model (Part 1: Development Management)—detailing its structure, key domains such as requirement management, test case design, and project planning, and how organizations can assess and improve their AI engineering capabilities.

AI GovernanceCapability Maturity ModelMLOps
0 likes · 9 min read
Unlocking AI Success: A Deep Dive into the Model/MLOps Capability Maturity Framework
DataFunTalk
DataFunTalk
May 31, 2022 · Artificial Intelligence

Using DolphinScheduler OpenMLDB Task for End‑to‑End MLOps Workflow

This article introduces the DolphinScheduler OpenMLDB Task, explains how it integrates OpenMLDB's feature platform into DolphinScheduler workflows to create a complete MLOps pipeline, and provides a step‑by‑step demonstration using the TalkingData ad‑fraud detection dataset from Kaggle.

DolphinSchedulerMLOpsOpenMLDB
0 likes · 7 min read
Using DolphinScheduler OpenMLDB Task for End‑to‑End MLOps Workflow
Efficient Ops
Efficient Ops
Apr 29, 2022 · Artificial Intelligence

China’s First AI Model Development Standard – Highlights from the AI Engineering Forum

The AI Engineering Online Forum, co‑hosted by China Academy of Information and Communications Technology, unveiled the industry’s first AI Model Development and Management (Model/MLOps) maturity standard, featured expert insights from finance, telecom, and tech leaders, and showcased practical MLOps implementations across banking, Huawei, and AI startups.

AIForumMLOps
0 likes · 6 min read
China’s First AI Model Development Standard – Highlights from the AI Engineering Forum
Code DAO
Code DAO
Apr 26, 2022 · Artificial Intelligence

Building an Open-Source ML Pipeline – Part 1: Data Ingestion & Storage

This article walks through building the first stage of an open‑source MLOps pipeline—data ingestion and storage—by outlining requirements, selecting tools such as Argo Workflows, Minio and Great Expectations, showing how to set up a minikube cluster, and providing Python scripts and an Argo CronWorkflow to extract, transform, and load OpenAQ air‑quality data into Minio.

Argo WorkflowsKubernetesMLOps
0 likes · 10 min read
Building an Open-Source ML Pipeline – Part 1: Data Ingestion & Storage
Efficient Ops
Efficient Ops
Apr 24, 2022 · Artificial Intelligence

How ModelOps and MLOps Accelerate AI Project Development

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.

AI EngineeringContinuous DeploymentMLOps
0 likes · 1 min read
How ModelOps and MLOps Accelerate AI Project Development
DataFunTalk
DataFunTalk
Jan 25, 2022 · Cloud Native

Model Deployment Challenges and a Seldon‑Based Cloud‑Native Solution

This article analyzes the complexities of deploying machine‑learning models in production, outlines the limitations of the existing ABox architecture, and details a comprehensive cloud‑native redesign using Seldon on Kubernetes—including custom HDFS initializers, GPU management, logging, and resource monitoring—to streamline operations and enable unified CPU/GPU model serving.

GPUKubernetesMLOps
0 likes · 12 min read
Model Deployment Challenges and a Seldon‑Based Cloud‑Native Solution
dbaplus Community
dbaplus Community
Jan 8, 2022 · Artificial Intelligence

How Ctrip Streamlined ML Model Development and Deployment with MLOps

This article explains how Ctrip tackled the long, costly ML model development‑to‑deployment pipeline by adopting and extending MLflow for full lifecycle management, covering model persistence, tracking, serving, custom pyfunc models, Dockerized deployment, scaling, and performance monitoring.

DockerFastAPIMLOps
0 likes · 14 min read
How Ctrip Streamlined ML Model Development and Deployment with MLOps
360 Tech Engineering
360 Tech Engineering
Jul 2, 2021 · Artificial Intelligence

DGL Operator: A Kubernetes‑Native Solution for Distributed Graph Neural Network Training

The article introduces DGL Operator, an open‑source Kubernetes‑based controller that automates the lifecycle of distributed graph neural network training with DGL, explains its terminology, challenges of native DGL distribution, and provides detailed architecture, workflow, and YAML/CLI examples for easy deployment.

AIDGLDistributed Training
0 likes · 18 min read
DGL Operator: A Kubernetes‑Native Solution for Distributed Graph Neural Network Training
DevOps
DevOps
Feb 9, 2021 · Operations

Choosing Between DataOps, MLOps, and AIOps: A Guide for Data Teams

The article examines how data teams can select the appropriate Ops framework—DataOps, MLOps, or AIOps—by comparing their origins, principles, responsibilities, and tooling, and stresses that cultural principles outweigh technology choices for efficient delivery of data and machine‑learning products.

AIOpsDataOpsDevOps
0 likes · 12 min read
Choosing Between DataOps, MLOps, and AIOps: A Guide for Data Teams
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 platformMLOpsSunfish
0 likes · 17 min read
Sunfish: An Integrated AI Platform for Model Training and Online Service Deployment at Youzan
360 Zhihui Cloud Developer
360 Zhihui Cloud Developer
Apr 23, 2019 · Artificial Intelligence

Mastering Kubeflow: Deploy AI Workflows on Kubernetes Step‑by‑Step

This article introduces Kubeflow, a Kubernetes‑based machine‑learning platform, outlines the typical ML lifecycle, details core components, explains why Kubernetes benefits AI workloads, and provides a step‑by‑step guide for installing and accessing Kubeflow’s services, concluding with its industry impact.

AI platformKubeflowKubernetes
0 likes · 7 min read
Mastering Kubeflow: Deploy AI Workflows on Kubernetes Step‑by‑Step