Tagged articles

AI Engineering

126 articles · Page 2 of 2

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 EngineeringCareer AdviceSFT

0 likes · 7 min read

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

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 EngineeringInference OptimizationPython scripting

0 likes · 8 min read

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

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 EngineeringAI agentsLLM

0 likes · 41 min read

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

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

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 AILarge Language Models

0 likes · 20 min read

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

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

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

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 EngineeringI/O optimizationLarge Language Models

0 likes · 15 min read

Mastering Large Language Model Training: Key Challenges and Optimization Strategies

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 optimizationLarge Language Models

0 likes · 13 min read

Challenges and Optimization Techniques for Large Language Model Training

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

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 EngineeringAI agents

0 likes · 9 min read

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

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

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

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 EngineeringRecommendation Systemsarchitecture

0 likes · 6 min read

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 AIMultimodal Learning

0 likes · 9 min read

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

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

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 EngineeringContinuous IntegrationMLOps

0 likes · 15 min read

Why MLOps Is the Key to Scalable AI Projects

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 EngineeringEfficiencyModel Scaling

0 likes · 10 min read

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

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

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

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

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 EngineeringDockerModel Training

0 likes · 12 min read

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

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 Engineeringcareer roadmap

0 likes · 5 min read

Roadmap to Becoming a Machine Learning Engineer by 2020

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

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

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