How DeepInsight Transforms Deep Learning Model Debugging with Real-Time Visualization

DeepInsight is a distributed, micro‑service‑based platform that provides end‑to‑end data exposure, multi‑dimensional visual analysis, and interactive debugging for TensorFlow models, turning opaque neural networks into transparent, controllable systems through real‑time visualizations, dynamic data sets, and integrated lifecycle management.

AI PlatformDeep LearningTensorFlow

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How DeepInsight Transforms Deep Learning Model Debugging with Real-Time Visualization

Ctrip Technology

Jun 11, 2018 · Artificial Intelligence

Ctrip Model Engine Platform: An Integrated End‑to‑End Service for Real‑Time AI Model Deployment

The article introduces Ctrip’s Model Engine Platform, a comprehensive system that streamlines feature preparation, engineering, model management, and product orchestration to enable fast, reliable real‑time AI model serving across various business scenarios, while addressing common challenges such as manual data handling, offline‑only prediction, and long development cycles.

AI PlatformCtripFeature Management

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Ctrip Model Engine Platform: An Integrated End‑to‑End Service for Real‑Time AI Model Deployment

Tencent Cloud Developer

May 29, 2018 · Artificial Intelligence

Intelligent Titanium TI-ONE: Tencent Cloud's One-Stop Machine Learning IDE

Intelligent Titanium TI-ONE is a one‑stop ML IDE on Tencent Cloud offering integrated data preparation, drag‑and‑drop algorithm development, automatic hyperparameter tuning, multi‑level collaboration, one‑click model deployment, and support for major frameworks such as TensorFlow, PyTorch, Angel and XGBoost, plus commercial features via GaiaStack.

AI PlatformMachine Learning PlatformModel Deployment

0 likes · 10 min read

Intelligent Titanium TI-ONE: Tencent Cloud's One-Stop Machine Learning IDE

Alibaba Cloud Developer

Feb 5, 2018 · Artificial Intelligence

How Alibaba’s AliMe Evolved in 2017: AI Architecture, Algorithms, and Real‑World Impact

In 2017 Alibaba's AliMe chatbot platform expanded from a single‑company solution to a multilingual, multi‑channel AI service, introducing platform‑level SaaS/PaaS capabilities, a seven‑layer front‑end architecture, modular back‑end design, advanced intent recognition, knowledge‑graph‑driven product management, reinforcement‑learning‑based recommendation, and machine‑reading comprehension for enterprise and consumer use cases.

AI PlatformAlibabaChatbot

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How Alibaba’s AliMe Evolved in 2017: AI Architecture, Algorithms, and Real‑World Impact

21CTO

Sep 17, 2017 · Artificial Intelligence

Scaling JD’s AI Platform: 5K+ Containers, GPU Management, and Multi‑Tenant Kubernetes

Since September 2016, JD’s AI foundation platform has leveraged Docker and Kubernetes to build a scalable machine‑learning infrastructure that now runs over 5,000 container instances, supports more than 20 AI services, and provides unified GPU, storage, networking, and multi‑tenant capabilities for both inference and training workloads.

AI PlatformGPU schedulingKubernetes

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Scaling JD’s AI Platform: 5K+ Containers, GPU Management, and Multi‑Tenant Kubernetes

21CTO

Aug 20, 2017 · Cloud Native

How JD Built a Scalable AI Platform on Kubernetes: Architecture, Networking, and Storage Insights

This article details JD's AI platform built on Docker and Kubernetes, covering its high‑availability architecture, network plugin choices, storage solutions like GlusterFS and SeaweedFS, GPU management, CI/CD pipelines, logging, monitoring, and native Spark on Kubernetes, illustrating how a cloud‑native stack supports large‑scale AI services.

AI PlatformCloud NativeGPU Management

0 likes · 14 min read

How JD Built a Scalable AI Platform on Kubernetes: Architecture, Networking, and Storage Insights