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Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Aug 7, 2025 · Big Data

How Flink ML Transforms Intelligent Operations: Real‑Time Anomaly Detection, Forecasting & Log Clustering

This article explains how Alibaba Cloud’s big‑data platform leverages Flink ML to build an intelligent‑operations service that tackles stability, cost and efficiency challenges through time‑series anomaly detection, forecasting and streaming log‑clustering, dramatically reducing latency, complexity and operational overhead.

FlinkIntelligent OperationsLog Clustering
0 likes · 25 min read
How Flink ML Transforms Intelligent Operations: Real‑Time Anomaly Detection, Forecasting & Log Clustering
Code DAO
Code DAO
Apr 20, 2022 · Artificial Intelligence

Hierarchical Latent Factor Deep Generative Model for Time‑Series Anomaly Detection

The article presents DGHL, a deep generative model that uses a ConvNet generator and alternating back‑propagation to learn hierarchical latent factors for online detection of point and subsequence anomalies in multivariate time‑series, handling missing data and achieving state‑of‑the‑art F1 scores on several benchmark datasets.

alternating backpropagationdeep generative modelhierarchical latent factors
0 likes · 10 min read
Hierarchical Latent Factor Deep Generative Model for Time‑Series Anomaly Detection
High Availability Architecture
High Availability Architecture
Oct 22, 2020 · Artificial Intelligence

AIOps at Meituan: Architecture, Design, and Practice of the Horae Time‑Series Anomaly Detection System

This article presents Meituan's AIOps exploration, focusing on the design and implementation of the Horae time‑series anomaly detection platform, covering background, technical roadmap, fault‑discovery workflow, time‑series classification, feature engineering, model training, real‑time detection, and future directions.

HoraeMeituanaiops
0 likes · 31 min read
AIOps at Meituan: Architecture, Design, and Practice of the Horae Time‑Series Anomaly Detection System
Meituan Technology Team
Meituan Technology Team
Oct 15, 2020 · Artificial Intelligence

AIOps at Meituan: Architecture and Practice of Time‑Series Anomaly Detection (Part 1)

Meituan’s AIOps initiative replaces manual rule‑based monitoring with the Horae platform, which automatically classifies time‑series metrics, applies CNN and XGBoost models to detect periodic anomalies, achieves over 90 % precision in production, and paves the way for broader metric types, forecasting, and advanced fault‑localization.

HoraeMeituanOperations
0 likes · 33 min read
AIOps at Meituan: Architecture and Practice of Time‑Series Anomaly Detection (Part 1)
Efficient Ops
Efficient Ops
Aug 28, 2018 · Operations

How to Detect and Resolve Time‑Series Anomalies in Modern AIOps

This article explains practical approaches for time‑series anomaly detection, multi‑dimensional drill‑down analysis, alarm‑convergence root‑cause analysis, and future AIOps planning, combining statistical methods, unsupervised learning, and supervised models to improve monitoring accuracy and operational efficiency.

OperationsRoot Cause AnalysisUnsupervised Learning
0 likes · 20 min read
How to Detect and Resolve Time‑Series Anomalies in Modern AIOps