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AI Engineering

160 articles · Page 2 of 2
AI Engineer Programming
AI Engineer Programming
Mar 16, 2026 · Artificial Intelligence

Why “Agent Development” Misleads: Framework vs. Harness in LLM Agents

The article explains that the term “Agent development” hides a fundamental split between Agent Frameworks, which give developers building blocks to assemble their own agents, and Agent Harnesses, which provide ready‑to‑run agents, and shows how this distinction affects decisions, maintenance, and troubleshooting.

AI EngineeringAgentClaude Code
0 likes · 10 min read
Why “Agent Development” Misleads: Framework vs. Harness in LLM Agents
AI Architecture Hub
AI Architecture Hub
Mar 10, 2026 · Industry Insights

How OpenAI Built a Million‑Line Codebase Without Human Typing – Lessons for AI‑Driven Software Engineering

OpenAI’s five‑month "Harness Engineering" experiment showed that a three‑person team could generate a million‑line software product entirely with Codex and GPT‑5, achieving ten‑fold productivity, redefining engineering roles, workflow loops, and offering five practical guidelines for AI‑augmented development while highlighting unresolved challenges.

AI EngineeringAI productivityHarness Engineering
0 likes · 18 min read
How OpenAI Built a Million‑Line Codebase Without Human Typing – Lessons for AI‑Driven Software Engineering
AI Architecture Hub
AI Architecture Hub
Mar 6, 2026 · Artificial Intelligence

How Agent Skills Solve AI Prompt Drift and Enable Scalable AI Workflows

This article analyzes the prompt‑drift problem in AI workflows, explains the open‑format Agent Skills standard, dissects a concrete Python code‑audit skill design, compares Anthropic and OpenAI ecosystems, and provides practical guidelines for building high‑availability, version‑controlled Agent Skills.

AI EngineeringAgent SkillsAnthropic
0 likes · 15 min read
How Agent Skills Solve AI Prompt Drift and Enable Scalable AI Workflows
Architect
Architect
Feb 28, 2026 · Artificial Intelligence

Designing Agent Tools: Key Lessons from Claude Code’s Action Space

This article distills the Claude Code team's hard‑won insights on building effective AI agents, highlighting why action‑space design outweighs model size, how structured questioning improves bandwidth, when to replace Todos with Tasks, and a repeatable seven‑step loop for evolving toolsets.

AI EngineeringAction Spaceprompt engineering
0 likes · 20 min read
Designing Agent Tools: Key Lessons from Claude Code’s Action Space
AI Large Model Application Practice
AI Large Model Application Practice
Feb 19, 2026 · Artificial Intelligence

When Should You Add a Knowledge Graph? 6 Practical Decision Criteria

This article outlines six concrete criteria—relationship‑centric data, reproducible reasoning, evolving schemas, multi‑hop queries, explainable decisions, and cross‑system data integration—to help engineers decide whether a knowledge graph is the right solution or if a relational database will suffice.

AI EngineeringData IntegrationGraph Databases
0 likes · 15 min read
When Should You Add a Knowledge Graph? 6 Practical Decision Criteria
Yunqi AI+
Yunqi AI+
Feb 13, 2026 · Artificial Intelligence

AI Engineering: Methodology and Practice for Turning Generative AI into Production Systems

The article outlines a comprehensive AI engineering methodology—including the TPMR framework, an AI‑driven development lifecycle, talent transformation from co‑pilot to AI pilot, and a practical enterprise adoption roadmap—to move generative AI and large models from experimental prototypes to production‑grade systems.

AI EngineeringAI lifecycleLLMOps
0 likes · 5 min read
AI Engineering: Methodology and Practice for Turning Generative AI into Production Systems
Baobao Algorithm Notes
Baobao Algorithm Notes
Feb 3, 2026 · Industry Insights

How One Developer Merges 600 Commits a Day with AI: Inside the Clawdbot Workflow

In this in‑depth interview, Peter Steinberger explains how AI agents let him submit and merge hundreds of commits daily, replace traditional code reviews with prompt‑driven requests, and redesign his development workflow around a closed‑loop validation system that reshapes modern software engineering.

AI EngineeringCode Reviewagent workflow
0 likes · 85 min read
How One Developer Merges 600 Commits a Day with AI: Inside the Clawdbot Workflow
High Availability Architecture
High Availability Architecture
Feb 1, 2026 · Artificial Intelligence

10 Proven Strategies to Become an AI Engineer with Claude Code

This guide shares ten practical techniques—from parallel git worktrees and plan‑mode prompting to custom Skills, sub‑agents, and terminal tweaks—that let developers treat Claude as a fully orchestrated AI engineering system, boosting productivity and reducing manual coding overhead.

AI EngineeringClaude Codeautomation
0 likes · 12 min read
10 Proven Strategies to Become an AI Engineer with Claude Code
Architecture and Beyond
Architecture and Beyond
Jan 17, 2026 · Artificial Intelligence

Progressive Disclosure & Dynamic Context: Making LLM Agents Reliable Execution Systems

This article explains how progressive disclosure and dynamic context management address the three core bottlenecks of complex LLM agents—context explosion, tool overload, and uncontrolled execution—by structuring context, tools, and SOPs into layered, token‑efficient, and verifiable workflows.

AI EngineeringDynamic ContextLLM agents
0 likes · 15 min read
Progressive Disclosure & Dynamic Context: Making LLM Agents Reliable Execution Systems
Tencent Technical Engineering
Tencent Technical Engineering
Jan 9, 2026 · Artificial Intelligence

Why Traditional Spec‑Driven Tools Fail and How AI‑Powered Context Engineering Can Supercharge Development

This article analyzes the shortcomings of spec‑driven tools like speckit and openspec in complex enterprise environments, introduces context engineering and compound engineering concepts, and presents a practical AI‑engineered workflow that reduces marginal cost, captures knowledge automatically, and makes AI tools feel invisible to developers.

AI EngineeringCompound EngineeringContext Engineering
0 likes · 48 min read
Why Traditional Spec‑Driven Tools Fail and How AI‑Powered Context Engineering Can Supercharge Development
DaTaobao Tech
DaTaobao Tech
Jan 5, 2026 · Artificial Intelligence

Why AI Engineering Isn’t a Reinvention of Software Architecture – Insights from AI Search

The article examines how AI engineering builds on, rather than discards, traditional software engineering principles, using the evolution of AI‑driven search at Alibaba to illustrate architectural upgrades that manage uncertainty, integrate context engineering, and combine classic design patterns with new AI‑specific tools.

AI EngineeringContext EngineeringMulti-agent
0 likes · 21 min read
Why AI Engineering Isn’t a Reinvention of Software Architecture – Insights from AI Search
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Nov 22, 2025 · Artificial Intelligence

Why Your RAG System Slows Down Over Time and How to Fix It

The article explains why a production Retrieval‑Augmented Generation (RAG) system becomes slower as it runs—due to growing embedding costs, expanding vector databases, heavier re‑ranking, and larger prompts—and provides concrete engineering optimizations such as batching, async concurrency, caching, partitioned retrieval, HNSW tuning, replica scaling, answer caching, and prompt sparsification to keep performance stable.

AI EngineeringPerformance OptimizationRAG
0 likes · 10 min read
Why Your RAG System Slows Down Over Time and How to Fix It
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Nov 21, 2025 · Artificial Intelligence

How to Build a Multi‑Layer Cache for Dynamic RAG Systems

This article explains why dynamic Retrieval‑Augmented Generation (RAG) requires a layered caching strategy rather than simple result caching, details a four‑level cache architecture—including embedding, search, answer, and pipeline caches—provides practical key‑generation and TTL guidelines, and outlines dirty‑data defenses to keep caches consistent and performant.

AI EngineeringCachingLLM
0 likes · 10 min read
How to Build a Multi‑Layer Cache for Dynamic RAG Systems
JD Tech
JD Tech
Nov 6, 2025 · Artificial Intelligence

LLMs Revolutionize Recommendation Systems: From Generative Models to Production

This article surveys the evolution of generative recommendation systems powered by large language models, detailing their technical foundations, engineering challenges, recent breakthroughs, and future research directions, while highlighting why the paradigm shift is occurring now.

AI EngineeringLLMgenerative recommendation
0 likes · 30 min read
LLMs Revolutionize Recommendation Systems: From Generative Models to Production
JD Tech Talk
JD Tech Talk
Oct 27, 2025 · Artificial Intelligence

How Large Language Models Are Revolutionizing Generative Recommendation Systems

Over the past year, generative recommendation has made substantial progress by leveraging large language models' powerful sequence modeling and reasoning abilities, introducing a new paradigm that replaces complex handcrafted features, addresses traditional recommendation bottlenecks, and outlines the evolution, core technologies, engineering challenges, and future directions of LLM‑based recommendation systems.

AI EngineeringEncoder-DecoderLLM
0 likes · 29 min read
How Large Language Models Are Revolutionizing Generative Recommendation Systems
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
Instant Consumer Technology Team
Instant Consumer Technology Team
Sep 30, 2025 · Artificial Intelligence

What Makes Youtu-GraphRAG’s Engineering Stand Out? Inside the AI Blueprint

This article dissects the engineering of Tencent's Youtu-GraphRAG, covering its architectural challenges, real‑time FastAPI/WebSocket design, security measures, iterative retrieval chains, parallel processing, intelligent caching, schema‑driven knowledge handling, and performance tweaks, offering practical insights for AI system builders.

AI EngineeringFastAPIGraphRAG
0 likes · 7 min read
What Makes Youtu-GraphRAG’s Engineering Stand Out? Inside the AI Blueprint
AI2ML AI to Machine Learning
AI2ML AI to Machine Learning
Sep 28, 2025 · Artificial Intelligence

Core Metrics for Enterprise Large‑Model Engineering

The article outlines the five essential engineering domains—application, model, compute, knowledge, and data—in the era of large models, and details concrete scale, efficiency, service, value, quality, and security metrics that enterprises should track to drive intelligent outcomes.

AI EngineeringKnowledge ManagementModel Performance
0 likes · 7 min read
Core Metrics for Enterprise Large‑Model Engineering
21CTO
21CTO
Sep 21, 2025 · Artificial Intelligence

Andrew Ng Explains the AI Skills Gap and How to Become an In‑Demand AI Engineer

Andrew Ng highlights a growing mismatch in the tech job market: while companies scramble for AI‑savvy developers, many new CS graduates struggle to find work, and he outlines the core AI‑driven capabilities—prompt engineering, RAG, model evaluation, and rapid prototyping—that separate successful AI engineers from the rest.

AIAI Engineeringjob market
0 likes · 6 min read
Andrew Ng Explains the AI Skills Gap and How to Become an In‑Demand AI Engineer
Alibaba Cloud Developer
Alibaba Cloud Developer
Sep 15, 2025 · Artificial Intelligence

Why MCP Isn't a Magic AI Upgrade: Deep Dive into Its Architecture, Host Role, and Real Costs

This article debunks common misconceptions about the Model Context Protocol (MCP), explains its client‑host‑server (CHS) architecture, shows how the Host drives AI decisions while Server and Client remain model‑agnostic, compares MCP with Function Calling, analyzes SDK source code, evaluates practical trade‑offs, and outlines the true engineering value and costs of using MCP in AI applications.

AI EngineeringFunction CallingLLM
0 likes · 35 min read
Why MCP Isn't a Magic AI Upgrade: Deep Dive into Its Architecture, Host Role, and Real Costs
ShiZhen AI
ShiZhen AI
Sep 5, 2025 · Artificial Intelligence

Andrew Ng Highlights Core AI Engineer Skills Amidst Major AI Industry Updates

The article reports that ChatGPT now supports branch conversations, Anthropic restricts service use in certain regions, Andrew Ng outlines essential AI engineer capabilities such as AI‑assisted software building, prompting and agentic workflows, and highlights the market demand, while also covering the Kimi K2 model upgrade, Hugging Face’s FineVision dataset release, and Google’s AI‑driven Deep Loop Shaping method published in *Science*.

AI EngineeringAI for astronomyAI safety
0 likes · 8 min read
Andrew Ng Highlights Core AI Engineer Skills Amidst Major AI Industry Updates
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Aug 8, 2025 · Artificial Intelligence

Unlocking Big Data Ops with Large Models: Opportunities, Challenges, Design

This article summarizes a Cloud Summit talk where Alibaba Cloud’s AI expert Zhang Yingying explains how large language models can enhance big‑data intelligent operations, covering opportunities, challenges, RAG‑based Q&A, multi‑agent diagnostics, and the engineering architecture needed for reliable, scalable deployment.

AI EngineeringRAGbig data operations
0 likes · 20 min read
Unlocking Big Data Ops with Large Models: Opportunities, Challenges, Design
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Aug 8, 2025 · Artificial Intelligence

From Chain‑of‑Thought to Self‑Evolving Agents: Lessons from Alibaba’s Intelligent Ops

This article traces the evolution of Alibaba’s intelligent agents from the initial chain‑of‑thought design through instantiation, structuring, self‑evolution, and middleware integration, highlighting practical challenges, architectural refinements, and open‑source tools for large‑scale AI operations.

AI EngineeringAgentmiddleware
0 likes · 16 min read
From Chain‑of‑Thought to Self‑Evolving Agents: Lessons from Alibaba’s Intelligent Ops
Software Engineering 3.0 Era
Software Engineering 3.0 Era
Jul 20, 2025 · Artificial Intelligence

From Vibe Coding to Kiro Spec-Driven Development: Bringing Engineering Determinism to AI Projects

The article contrasts the chaotic, prompt‑driven "Vibe coding" approach with Kiro's Spec‑driven development, explaining how a two‑session model—Vibe for creative exploration and Spec for deterministic engineering—structures AI projects from project‑level documentation to runtime execution, improving reliability and traceability.

AI EngineeringKiroRunner
0 likes · 10 min read
From Vibe Coding to Kiro Spec-Driven Development: Bringing Engineering Determinism to AI Projects
Smart Era Software Development
Smart Era Software Development
Jun 16, 2025 · Artificial Intelligence

Key Skills That Define the Next‑Gen GenAI Application Engineer

The article outlines the core competencies of a next‑generation GenAI application engineer—rapid modular AI development, leveraging AI‑assisted coding tools, strong product and design intuition, and mastery of a layered “AI LEGO” stack ranging from prompting and RAG to agentic frameworks and multimodal technologies.

AI EngineeringAI toolsAgentic Frameworks
0 likes · 5 min read
Key Skills That Define the Next‑Gen GenAI Application Engineer
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
Java Architecture Diary
Java Architecture Diary
May 19, 2025 · Artificial Intelligence

How Ollama 0.7 Unlocks Local Multimodal AI with One Command

Ollama 0.7 introduces a fully re‑engineered core that brings seamless multimodal model support, lists top visual models, showcases OCR and image analysis capabilities, explains technical breakthroughs, and provides a quick three‑step guide to deploy powerful local AI vision.

AI EngineeringAI modelsOllama
0 likes · 7 min read
How Ollama 0.7 Unlocks Local Multimodal AI with One Command
DevOps
DevOps
Apr 22, 2025 · Artificial Intelligence

How to Think About Agent Frameworks: A Critical Review of Design Patterns, Challenges, and LangGraph

This article critically examines popular agent frameworks, compares OpenAI and Anthropic definitions, highlights the core difficulty of maintaining proper context for reliable agents, and presents LangGraph’s declarative and imperative features along with practical guidance for building production‑grade agent systems.

AI EngineeringAgent FrameworksAgent Systems
0 likes · 24 min read
How to Think About Agent Frameworks: A Critical Review of Design Patterns, Challenges, and LangGraph
DaTaobao Tech
DaTaobao Tech
Feb 21, 2025 · Artificial Intelligence

AI-Powered Face Swapping for the Spring Festival Gala: System Design and Deployment

The paper details the design and deployment of an AI‑driven face‑swap platform for the 2025 CCTV Spring Festival Gala, featuring a dual‑model SDXL pipeline with ControlNet and LoRA fine‑tuning, optimized preprocessing and GPU‑specific acceleration to achieve sub‑3‑second latency at over 10 k QPS, supporting scaling, throttling, and multi‑region load balancing, and ultimately serving ten million users and generating hundreds of millions of personalized gala images.

AI EngineeringAIGCSpring Festival Gala
0 likes · 28 min read
AI-Powered Face Swapping for the Spring Festival Gala: System Design and Deployment
21CTO
21CTO
Dec 18, 2024 · Artificial Intelligence

5 AI Engineering Trends Shaping 2024: Agents, Coding Tools, and the Rise of Small Models

The 2024 AI engineering landscape is defined by mature AI coding assistants, the surge of AI agents like LangChain and LlamaIndex, the emergence of small, locally‑hosted language models, the solidifying role of AI engineers, and heated debates over what truly counts as open‑source AI.

AI EngineeringAI coding toolssmall language models
0 likes · 9 min read
5 AI Engineering Trends Shaping 2024: Agents, Coding Tools, and the Rise of Small Models
Baobao Algorithm Notes
Baobao Algorithm Notes
Aug 29, 2024 · Industry Insights

Why Pretraining Boosts New Engineers More Than SFT: A Practical Guide

The answer argues that fresh graduates should join pre‑training teams because the required engineering tasks—large‑scale data crawling, Hadoop/Spark pipelines, torch and CUDA setup, megatron code debugging, and scaling‑law experiments—rapidly sharpen coding skills, while SFT work focuses mainly on data labeling and offers slower technical growth.

AI EngineeringPretrainingSFT
0 likes · 7 min read
Why Pretraining Boosts New Engineers More Than SFT: A Practical Guide
Baobao Algorithm Notes
Baobao Algorithm Notes
Aug 26, 2024 · Artificial Intelligence

Master Essential LLM Engineering Skills: Transform, Model, and Infer with Custom Scripts

This guide presents a hands‑on curriculum of core large‑model engineering tasks—including model conversion scripts, custom modeling wrappers, multi‑model inference utilities, and channel‑aware loss tracking—to help practitioners build practical, reusable tools without deep theoretical overhead.

AI EngineeringPython scriptinginference optimization
0 likes · 8 min read
Master Essential LLM Engineering Skills: Transform, Model, and Infer with Custom Scripts
JD Tech
JD Tech
May 31, 2024 · Artificial Intelligence

Understanding Large Language Models, Retrieval‑Augmented Generation, and AI Agents: Concepts, Engineering Practices, and Applications

This article explains the fundamentals and engineering practices of large language models (LLM), retrieval‑augmented generation (RAG) and AI agents, compares small and large embedding models, provides Python code for vector‑database RAG with Chroma, and discusses integration, use cases, and future challenges in AI development.

AI AgentsAI EngineeringLLM
0 likes · 41 min read
Understanding Large Language Models, Retrieval‑Augmented Generation, and AI Agents: Concepts, Engineering Practices, and Applications
G7 EasyFlow Tech Circle
G7 EasyFlow Tech Circle
May 29, 2024 · Artificial Intelligence

Engineering Large Model Enterprise Applications: Best Practices

This article outlines the key characteristics of large‑model enterprise applications, compares them with consumer use cases, and presents a comprehensive engineering roadmap—including model selection, knowledge‑base integration, tool implementation, intent recognition, output control, high‑availability deployment, and ongoing optimization—to help practitioners effectively harness AI models in real‑world business environments.

AI EngineeringRAGlarge model
0 likes · 12 min read
Engineering Large Model Enterprise Applications: Best Practices
DevOps
DevOps
Apr 17, 2024 · Artificial Intelligence

Engineering Capabilities for Enterprise Large Model Applications: Prompt Engineering, RAG, and Fine‑Tuning

The article explores how enterprises can build and improve large‑model applications by combining prompt engineering, retrieval‑augmented generation (RAG), and fine‑tuning, discusses their relationships, optimization dimensions, testing challenges, and provides practical guidance for SE4AI implementation.

AI EngineeringEnterprise AIFine-tuning
0 likes · 20 min read
Engineering Capabilities for Enterprise Large Model Applications: Prompt Engineering, RAG, and Fine‑Tuning
HelloTech
HelloTech
Apr 10, 2024 · Artificial Intelligence

An Overview of LangChain: Architecture, Core Components, and Code Examples

LangChain is an open‑source framework that provides Python and JavaScript SDKs, templates, and services such as LangServe and LangSmith to compose models, embeddings, prompts, indexes, memory, chains, and agents via a concise expression language, enabling rapid prototyping, debugging, and deployment of LLM‑driven applications.

AI EngineeringAgentsJavaScript
0 likes · 19 min read
An Overview of LangChain: Architecture, Core Components, and Code Examples
Alibaba Cloud Developer
Alibaba Cloud Developer
Apr 10, 2024 · Artificial Intelligence

Master LangChain in 10 Minutes: From Basics to Advanced AI Engineering

This guide walks AI engineers through a rapid 10‑minute boot‑strap of LangChain, explaining its purpose, core concepts, design questions, environment setup, and step‑by‑step code examples that cover APIs, chains, memory, retrieval‑augmented generation, tools, agents, and the overall architecture.

AI EngineeringAgentsLLM
0 likes · 28 min read
Master LangChain in 10 Minutes: From Basics to Advanced AI Engineering
NewBeeNLP
NewBeeNLP
Mar 21, 2024 · Artificial Intelligence

Mastering Large Language Model Training: Key Challenges and Optimization Strategies

This article examines the resource and efficiency challenges of scaling large language model training, explains data, model, pipeline, and tensor parallelism, and provides practical I/O, communication, and stability optimization techniques—including high‑availability storage, RDMA networking, NCCL tuning, and fault‑tolerant recovery—to improve throughput and reliability.

AI EngineeringDistributed TrainingI/O optimization
0 likes · 15 min read
Mastering Large Language Model Training: Key Challenges and Optimization Strategies
DataFunTalk
DataFunTalk
Mar 20, 2024 · Artificial Intelligence

Challenges and Optimization Techniques for Large Language Model Training

The article outlines the resource and efficiency challenges of scaling large language models, explains data and model parallelism strategies, and details practical I/O, communication, and stability optimizations—including high‑availability storage, RDMA networking, and fault‑tolerance measures—to improve training throughput and reliability.

AI EngineeringI/O optimizationcommunication optimization
0 likes · 13 min read
Challenges and Optimization Techniques for Large Language Model Training
Architecture & Thinking
Architecture & Thinking
Jan 14, 2024 · Artificial Intelligence

How Baidu Scales Content Understanding to Trillions of Pages with AI Engineering

This article explains how Baidu processes internet‑scale content by applying deep AI‑driven understanding, detailing cost‑optimization, efficiency improvements, model‑service frameworks, resource‑scheduling systems, and batch‑compute platforms that together enable trillion‑level indexing and feature extraction.

AI EngineeringBatch ComputingHTAP storage
0 likes · 16 min read
How Baidu Scales Content Understanding to Trillions of Pages with AI Engineering
21CTO
21CTO
Dec 15, 2023 · Artificial Intelligence

Why 2024 Will Be the Year of AI Engineers and LLM‑Driven Apps

The article outlines five major AI engineering trends for 2024—including the rise of AI engineers, evolving LLM tech stacks, open‑source large models, vector databases, and AI agents—highlighting how these shifts will reshape application development and industry competition.

2024 trendsAI AgentsAI Engineering
0 likes · 9 min read
Why 2024 Will Be the Year of AI Engineers and LLM‑Driven Apps
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Nov 8, 2023 · Big Data

How Big Data and AI Converge: Insights from Alibaba Cloud’s 2023 Conference

The talk outlines the evolution from model‑centric to data‑centric AI development, explains Alibaba Cloud’s integrated big data‑AI platform, showcases real‑world use cases like knowledge‑base QA and personalized recommendation, and details the underlying cloud‑native services that enable seamless data and AI collaboration.

AI Engineeringmodel training
0 likes · 16 min read
How Big Data and AI Converge: Insights from Alibaba Cloud’s 2023 Conference
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
Xiaohongshu Tech REDtech
Xiaohongshu Tech REDtech
Aug 25, 2023 · Artificial Intelligence

DataFunSummit 2023: Recommendation Systems Online Summit

The DataFunSummit 2023 online summit (August 26‑27) will explore eight recommendation‑system topics—including core and engineering architecture, model training/inference, large models, graphs, cold start, and multi‑task scenarios—featuring Xiaohongshu leaders who will present on graph‑based business architecture, integrated training‑inference pipelines, and user/content cold‑start strategies.

AI Engineeringarchitecturecold start
0 likes · 6 min read
DataFunSummit 2023: Recommendation Systems Online Summit
DataFunSummit
DataFunSummit
May 5, 2023 · Artificial Intelligence

Advances in Virtual Humans, Multimodal Technology, and General AI – Insights from OPPO

The article presents OPPO's latest research on virtual human audio‑lip and RGB driving, multimodal learning breakthroughs such as CETNETs and cross‑modal matching, and a reflective discussion on the challenges and future directions of general artificial intelligence, highlighting the interconnections among these three domains.

AI EngineeringGeneral AINeRF
0 likes · 9 min read
Advances in Virtual Humans, Multimodal Technology, and General AI – Insights from OPPO
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 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
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Nov 4, 2022 · Artificial Intelligence

How AI Platforms Turn Dreams into Reality: Scaling, Efficiency, and Usability

In this talk from the 2022 Yunqi Conference, Jia Yangqing explains how Alibaba's AI platform addresses efficiency, scale, and usability challenges by moving the Damo Academy to the cloud, open‑sourcing ModelScope, and delivering large‑model training, deployment, and inference services at massive scale.

AI EngineeringPlatformefficiency
0 likes · 10 min read
How AI Platforms Turn Dreams into Reality: Scaling, Efficiency, and Usability
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
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
Architects Research Society
Architects Research Society
Dec 3, 2020 · Artificial Intelligence

Gartner 2021 Top Strategic Technology Trends: AI Engineering, Distributed Cloud, Privacy Computing, and More

Gartner's 2021 strategic technology trends highlight the rise of behavior‑driven internet, total experience, privacy‑enhanced computing, distributed cloud, anywhere operation, security mesh, intelligent composable business, AI engineering, and hyper‑automation as key drivers for organizational resilience and growth.

AI EngineeringHyperautomationPrivacy Computing
0 likes · 10 min read
Gartner 2021 Top Strategic Technology Trends: AI Engineering, Distributed Cloud, Privacy Computing, and More
JD Tech Talk
JD Tech Talk
Nov 13, 2020 · Artificial Intelligence

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

This article provides a comprehensive engineering overview of federated learning, covering its core distributed‑learning concept, Docker‑based deployment, detailed training‑service architecture with validation, scheduling, metadata, and model‑management components, as well as a complete inference framework and workflow for production use.

AI Engineeringdistributed systemsdocker
0 likes · 12 min read
Practical Engineering Guide to Federated Learning: Deployment, Training, and Inference
Architects Research Society
Architects Research Society
Aug 9, 2020 · Artificial Intelligence

Roadmap to Becoming a Machine Learning Engineer by 2020

This article presents a set of charts outlining various learning paths and technologies for aspiring machine‑learning engineers, offering guidance on what to study and why, while also providing community resources for deeper discussion and support.

2020AI EngineeringLearning Path
0 likes · 5 min read
Roadmap to Becoming a Machine Learning Engineer by 2020
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
21CTO
21CTO
Jul 24, 2017 · Artificial Intelligence

Why Every AI Engineer Must Master System Architecture

The article explains that AI engineers need solid architecture knowledge to turn high‑performing algorithms into real‑world solutions, covering four key reasons: algorithm vs. problem solving, on‑site deployment challenges, scalability, and effective team collaboration.

AI EngineeringSoftware Engineeringmachine learning deployment
0 likes · 7 min read
Why Every AI Engineer Must Master System Architecture
21CTO
21CTO
Oct 26, 2015 · Artificial Intelligence

How Weibo’s Recommendation Engine Evolved: From 1.0 to Platform‑Scale 3.0

This article traces the evolution of Weibo's recommendation architecture across three major phases—independent 1.0, layered 2.0, and platform‑centric 3.0—detailing the driving business and technical factors, architectural components, advantages, shortcomings, and key outcomes of each stage.

AI EngineeringWeiboarchitecture evolution
0 likes · 19 min read
How Weibo’s Recommendation Engine Evolved: From 1.0 to Platform‑Scale 3.0