OpenAI announced a $4 billion‑backed Deployment Company to embed AI across core business functions and unveiled Daybreak, an AI‑powered network‑defense tool, signaling a shift from pure model provision to end‑to‑end enterprise AI deployment.
This expert roundtable dissects why RAG often fails in production—low recall, hallucinations, cost overruns—and walks through concrete diagnostics, hybrid search designs, knowledge‑engineering tricks, GraphRAG and Agentic RAG advances, plus practical deployment, security, and cost‑optimization guidelines.
This guide walks through installing Ollama on Windows 10, downloading a Chinese‑friendly LLM such as Qwen2, configuring a CRM’s application‑dev.yml to point to the local Ollama service, restarting the backend, and handling optional CORS settings, highlighting zero‑cost, privacy, and stability benefits.
This guide walks you through installing Python, obtaining a PPIO API key, installing the PPHermes CLI, launching a Hermes Agent sandbox in the cloud, and managing its lifecycle, with optional integration to Feishu/Lark and AI‑agent skill usage.
This article analyzes the rapid growth of large language models, presents a six‑dimensional classification framework, compares open‑source and closed‑source options, and offers a step‑by‑step selection checklist for enterprises seeking the most suitable model for their specific needs.
This article outlines a comprehensive, six‑part model engineering framework that transforms AI capabilities into reusable business functions, defines a stable technical stack, establishes model selection and architecture guidelines, implements rigorous control, data, and training processes, and explains how these layers synergize for reliable, scalable deployment.
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.
The article explains how scaling AI agents reveals fragmented, inconsistent internal documentation, and argues that high‑quality production knowledge bases require a company‑wide, role‑based process, concrete writing rules, continuous inspection, and cross‑department ownership to ensure AI answers remain accurate and user‑focused.
The article examines four common yet hidden model evaluation mistakes—confusing attractive metrics with business impact, using static test sets, ignoring statistical significance, and lacking fine‑grained attribution—illustrating each with real‑world cases and offering concrete practices to build a more robust, business‑aligned evaluation pipeline.
This article walks through enabling Hyper‑V on Windows 11 Pro, configuring Docker Desktop with Chinese mirrors, adjusting storage, installing Ubuntu via WSL, cloning and setting up Dify, running Docker Compose, and linking Ollama's LLM so the AI agent runs entirely on a local machine.
The article explains how OpenAI’s Forward Deployed Engineering (FDE) team bridges the gap between powerful models and real‑world value by embedding engineers on‑site, iterating over a 6‑week technical rollout followed by a 4‑month trust‑building phase, and using eval‑driven development to turn custom solutions into reusable products.
The article reviews the major 2025 breakthroughs in multimodal, open‑source, and deployment technologies for large models and outlines four 2026 trends—including ToC vs. ToB service split, dual‑hand data generation, MoE routing advances, and AI4Science breakthroughs—that will shape the next wave of AI development.
This guide walks through setting up a Ubuntu VM, installing Docker, pulling the AnythingLLM image, configuring storage, launching the container, and using it to ingest and query local documents with a DeepSeek‑R1 model.
nndeploy is an open‑source, high‑performance on‑device AI deployment framework that abstracts the repetitive “last‑mile” workflow into a visual drag‑and‑drop DAG, offering multi‑platform inference, optimization, and ready‑to‑use model configs, enabling developers to go from prototype to production in minutes.
Block, a fintech giant, deployed AI agents across all 12,000 staff in eight weeks by adopting the Model Context Protocol, simplifying installation, offering model choice, automating tool management, and building a supportive community, revealing key lessons for enterprise AI adoption.
This guide explains how to quickly deploy the open‑source Dify AI platform on Alibaba Cloud's Serverless Application Engine, covering the challenges of private deployment, the benefits of SAE's one‑click solution, and detailed step‑by‑step configuration to achieve a production‑grade AI service.
This article walks you through deploying JD Cloud’s open‑source JoyAgent AI agent, covering lightweight cloud host creation, opening firewall ports, configuring model and search engine settings, and finally starting the service to access the Genie interface via a public IP.
This article showcases a prize‑driven Q&A session where senior architects answer real‑world questions on AI deployment, backend language choices, large‑model integration, and career advancement, offering practical guidance and a chance to win exclusive Tencent Cloud merchandise.
This guide walks you through configuring OLLAMA for multi‑GPU load balancing, covering hardware checks, CUDA setup, native and Docker deployment methods, detailed parameter tuning, advanced sharding strategies, troubleshooting, performance optimization, and production‑grade monitoring to maximize throughput and stability of large language models.
This article walks developers through the entire process of deploying Dify locally, configuring model providers, creating and segmenting a knowledge base with RAG, choosing indexing methods, and integrating the knowledge base into a chatbot application, complete with code snippets and visual guides.
This article explains why quantizing massive AI models is essential for deploying them on resource‑constrained devices, outlines core quantization concepts, techniques, and methods, compares their pros and cons, and presents practical application scenarios such as smartphones, autonomous driving, IoT, and edge computing.
Enterprises rushing to purchase DeepSeek AI appliances and smart‑agent platforms often face hidden technical, data, and organizational challenges that turn promised "plug‑and‑play" solutions into costly missteps, highlighting the need for realistic strategy, robust data governance, and continuous capability building.
In a detailed interview, Zhao Chenyang explains how multimodal large models (VLM) overcome the limitations of traditional OCR in mixed layouts, table reconstruction, and handwritten text by leveraging self‑supervised pre‑training, lightweight fine‑tuning, and hybrid pipelines that dramatically cut annotation costs and improve recall rates.
The article explains what inference engines are, why they are needed beyond raw Python scripts, and outlines best practices such as model quantization, batching, and parallelism, while comparing popular open‑source and commercial options for production AI workloads.
The DeepSeek large language model has been extensively deployed across more than 100 central and local Chinese state‑owned enterprises, spanning sectors such as energy, manufacturing, transportation, finance, telecommunications, construction, and public services, driving intelligent transformation through applications like smart scheduling, risk assessment, intelligent customer service, and AI‑enhanced office automation.
This guide explains how MetaGPT’s multi‑agent architecture eliminates the logical gaps of single‑agent systems, improves task stability, and can be rapidly deployed on Alibaba Cloud’s Serverless Function AI platform with step‑by‑step instructions, configuration details, and example applications.
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.
This tutorial walks developers through setting up a low‑cost, containerized AI service on Windows by installing Docker, deploying Redis and Ollama containers, pulling the DeepSeek‑R1 model, and integrating everything with Spring AI to enable continuous conversation support.
This guide explains how to launch the ComfyUI Cluster Edition on Volcengine, covering its enterprise features such as multi‑user collaboration, resource isolation, built‑in plugins, flexible mounting, and step‑by‑step deployment using VKE, CP, and API Gateway to enable efficient, scalable AI image generation.
The article explains why most projects should start with prompt engineering or simple agent workflows, outlines the scenarios where model fine‑tuning adds real value, compares fine‑tuning with Retrieval‑Augmented Generation, and offers practical criteria for deciding which approach to adopt.
The article explains why combining powerful cloud AI models with lightweight edge models is essential for overcoming compute‑cost trade‑offs, privacy constraints, and scenario gaps, and provides a four‑step guide, real‑world case studies, and future directions for collaborative AI deployment.
The article analyzes DeepSeek‑R1’s low‑cost inference architecture, Chinese language optimizations, novel prompt‑engineering techniques, and the practical challenges of deploying large domestic models, offering insights into vertical AI applications and the evolving open‑source ecosystem in China.
This guide walks you through building a Retrieval‑Augmented Generation (RAG) system by integrating Alibaba Cloud's Hologres vector store, the Proxima high‑performance vector engine, and DeepSeek large language models via PAI‑EAS, covering prerequisites, deployment steps, configuration, and inference verification.
The article compares DeepSeek with ChatGPT, highlighting DeepSeek’s superior performance in math and reasoning, lower cost, hardware requirements, and how to locally deploy the R1 model using Ollama and a GUI, illustrating its potential to reshape the AI landscape.
This guide explains how to locally deploy the open‑source DeepSeek model using Ollama, enhance interaction with Chatbox and Page Assist, and connect a local knowledge base via AnythingLLM's RAG architecture, providing step‑by‑step instructions, hardware requirements, and API examples for a self‑hosted AI system.
DeepSeek‑R1 is a high‑performance AI inference model that leverages reinforcement‑learning techniques to boost reasoning on complex tasks, has become a Chinese‑New‑Year sensation, and requires substantial hardware resources for local deployment, especially the full‑scale 671‑billion‑parameter version.
This comprehensive guide examines the leading large‑model storage formats—including Hugging Face Transformers, TensorFlow SavedModel, ONNX, TorchScript, and GGUF—detailing their file structures, serialization methods, strengths, weaknesses, and typical use‑cases, helping developers and researchers select the optimal format for their specific AI workloads.
This guide explains how to privately deploy the DeepSeek large language model using a JD Cloud virtual computer, set up Ollama as the LLM service, run various model versions, and integrate local knowledge bases through CherryStudio, Page Assist, and AnythingLLM for offline and network‑enabled AI applications.
This guide walks you through registering on Silicon Flow, accessing DeepSeek models online, and using Cherry Studio to set up a private, low‑spec‑requirement deployment that keeps conversations stored locally for privacy and security.
This guide walks you through downloading Ollama, selecting the DeepSeek‑r1 model, installing it via a terminal command, and configuring ChatBoxAI for seamless local AI conversations, all with clear screenshots for each step.
This article introduces the 671‑billion‑parameter DeepSeek‑V3 Mixture‑of‑Experts LLM, explains the PAI‑Model Gallery platform that aggregates top AI models, and provides a step‑by‑step guide to deploy DeepSeek‑V3 on Alibaba Cloud’s PAI‑EAS service with zero‑code configuration.
This article examines the AIGC engineering architecture, detailing its data, model, fine‑tuning, inference, application, and monitoring layers, and explains the distinct responsibilities and challenges of application engineers, algorithm engineers, and “alchemy” specialists, highlighting how this structured approach accelerates generative AI productization.
This guide walks you through the full workflow of using DistilQwen2 on Alibaba Cloud's PAI platform, covering environment setup, model deployment, fine‑tuning with SFT/DPO, evaluation, compression, and distillation, while providing practical code snippets and resource links.
The article reflects on Alibaba Cloud's large‑model deployments, outlines four service scenarios, examines three fundamental questions about foundation models, and offers a prioritized roadmap—including prompt engineering, RAG, and organizational changes—to effectively bring LLMs to production.
Ant Group and the China Academy of Information and Communications Technology released a research report outlining a trustworthy application framework for large models in rigorous sectors such as finance and healthcare, detailing technical safeguards, industry case studies, and guidance for scalable, secure AI deployment.
This article explores the free GLM-4-Flash API from Zhipu AI, detailing its lightweight architecture, performance specs, a logic‑puzzle demonstration, and provides a comprehensive step‑by‑step tutorial—including data upload, model fine‑tuning, deployment commands and example code for building a LangChain‑based knowledge‑base retrieval system.
This guide walks you through deploying large language models with NVIDIA NIM on Volcengine's Kubernetes Engine (VKE), covering environment setup, model optimization, Helm chart deployment, monitoring integration, and the key advantages of using NIM as a cloud‑native AI micro‑service.
Learn how to quickly launch a Stable Diffusion WebUI service in just five minutes using Volcengine’s cloud‑native continuous delivery platform, which abstracts Kubernetes complexities, provides pre‑configured AI templates, serverless VCI deployment, automatic scaling, API gateway access, and includes a Python client for image generation.
This article examines the Model-as-a-Service (MaaS) paradigm, tracing its origins, defining its expanded capabilities for large‑model ecosystems, outlining the full‑stack services it offers, and analyzing current industry adoption, deployment models, and the technical and regulatory challenges that must be addressed for scalable AI rollout.
This guide explains how to quickly host the open‑source GPT‑SoVITS text‑to‑speech model on Alibaba Cloud Function Compute, covering its application scenarios, cloud‑native architecture, step‑by‑step deployment, voice training workflow, and how to generate speech using provided demos.
Qwen2, Alibaba Cloud's new open‑source LLM series, offers five model sizes with GQA acceleration, and through PAI‑QuickStart developers can zero‑code fine‑tune, evaluate, and deploy these models using cloud resources, SDKs, and OpenAI‑compatible APIs.
Meta’s newly released LLaMA 3 models (8B and 70B) boast record‑breaking performance, and this guide walks you through the community buzz, technical specs, and a detailed JD Cloud workflow—from provisioning a GPU instance to running the model in a Jupyter environment.
The article presents a 2024 AIGC round‑table where Chinese experts discuss whether to follow a technology‑first or market‑first approach, the challenges of compute, algorithms and data, domestic versus foreign large‑model strategies, multi‑model deployment in enterprises, and criteria for evaluating successful AIGC applications.
The article examines how enterprises can adopt domain‑specific large models by balancing demand‑side cost‑reduction needs with supply‑side mature training techniques, discusses team composition, fine‑tuning methods, data governance for unstructured data, and outlines Deepexi’s product ecosystem designed to improve efficiency, performance, and user experience.
The article examines how enterprises can adopt domain‑specific large models by addressing talent and cost challenges, outlining self‑supervised pre‑training, instruction fine‑tuning, data governance for unstructured data, dataset balance, model‑type selection, and integrated product solutions to achieve efficient, high‑performance AI deployments.
The article explores how enterprises can adopt domain‑specific large language models by addressing talent and cost challenges, outlining training pipelines, data governance for unstructured data, dataset balancing, fine‑tuning techniques, and a product ecosystem that lowers deployment barriers while optimizing performance and economics.
This guide walks you through installing Docker, configuring Docker‑Compose, setting up FastGPT’s config files, launching the containers, and creating a private knowledge base to enable AI‑driven question answering on your own server.
This article describes how Du Xiaoman tackled the high cost, instability, and long cycles of AI algorithm deployment by building the ATLAS automated machine learning platform, detailing its four‑stage workflow, component platforms, scaling and efficiency techniques, and practical Q&A for practitioners.
This article explores Meta's Llama 2 large language model—its performance, expanded training data, architectural details, evaluation results, RLHF fine‑tuning process, and step‑by‑step deployment on UCloud UK8S using Docker and Kubernetes—providing a comprehensive guide for AI practitioners.
This guide walks you through using Alibaba Cloud's PAI platform to quickly fine‑tune Llama 2 with LoRA or full‑parameter methods, deploy the models as online inference services, and launch an interactive WebUI, covering preparation, data formatting, training jobs, and deployment details.
This tutorial walks you through preparing a VKE cluster, pulling the Langchain‑ChatGLM container image, creating the necessary Deployment and Service resources, and adding a local knowledge base, enabling you to run a Langchain‑based ChatGLM service with GPU support on Volcengine’s cloud‑native platform.
This article explains how Shuhe Technology leveraged Alibaba Cloud's Serverless Kubernetes (ASK) and Knative to deploy, scale, and monitor over 500 AI model services—including Stable Diffusion—achieving 60% cost savings, zero‑to‑scale elasticity, and rapid deployment cycles.
Deploy Stable Diffusion on Tencent Cloud by building a Docker image, pushing it to TCR, creating a GPU‑enabled TKE cluster with CFS storage, configuring qGPU sharing, exposing the service via Cloud Native API Gateway, optimizing inference with TACO Kit, storing results in COS, and applying content moderation.
This guide shows how to map a Stable Diffusion container's dynamic paths to Alibaba Cloud NAS, configure Serverless Devs, deploy the application, and upload model files via a visual admin UI or NAS commands, enabling fast AI image generation on a cloud‑native platform.
This guide walks you through deploying the open‑source Stable Diffusion WebUI on Alibaba Cloud Function Compute using Serverless Devs, covering prerequisites, a single‑line deployment command, configuration details, access URL, and practical tips for handling GPU rendering and cold‑start latency.
This article details Du Xiaoman's development of the ATLAS automated machine learning platform, covering business scenarios, AI algorithm deployment challenges, the end‑to‑end production workflow, platform components such as annotation, data, training and deployment, as well as optimization techniques like AutoML, meta‑learning, NAS, and large‑scale parallelism, concluding with lessons learned and future directions.
FastDeploy is an open‑source toolkit that standardizes AI model APIs and enables developers to deploy vision, NLP, and speech models on diverse hardware—including GPUs, CPUs, Jetson, ARM, and various NPUs—using just three lines of code or a single command, while delivering end‑to‑end performance optimizations.
The article presents PaddlePaddle’s PP‑ShiTu‑based smart retail product recognition solution, detailing a complete pipeline—from data preparation and model optimization to low‑latency deployment—that overcomes high‑similarity packaging, rapid SKU changes, and costly retraining, achieving over 98 % Top‑1 recall with 0.2‑second CPU inference.
With the rapid rise of edge computing, deploying AI models for tasks like object detection, OCR, and speech recognition on resource‑constrained devices faces speed challenges; the upgraded Paddle Lite inference engine and PaddleSlim compression tools claim up to 23% faster inference and significant model size reductions, offering a practical solution.
Alibaba’s MNN, a lightweight high‑performance deep‑learning inference engine, earned top honors in China’s 2022 “Science & Innovation China” awards, and delivers impressive gains such as 350% speedup on X86 CPUs, 2.1‑2.3× acceleration on ARM with sparse models, plus integrated OpenCV/Numpy functionality for edge AI deployment.
The article shares a programmer's interview experience at Baidu, discussing how to adapt AI algorithms for edge deployment, balance model performance and efficiency, apply model compression techniques, and continuously iterate models, while also promoting an upcoming AI deployment online course.
By moving decoding, color conversion, preprocessing, inference, and re‑encoding entirely onto the GPU and enabling batch processing with flexible Python scripts, iQIYI’s video‑image enhancement service achieved ten‑fold throughput, over 90 % GPU utilization, and dramatically lower resource use, accelerating AI video inference deployment.
This article shares personal journeys of five Huawei AI experts, recommends essential AI books, walks through setting up PyCharm with ModelArts for hands‑on model training, and outlines a three‑stage AI career roadmap—from practical coding to mastering principles and deploying inference—offering actionable guidance for anyone looking to break into artificial intelligence.
This article explains how to deploy TensorFlow on Kubernetes, addressing resource isolation, GPU scheduling, and distributed training challenges by introducing a custom TensorFlow‑on‑K8s system with client, task, and autospec modules, plus container design for reliable job execution.
