From the article
VictoriaMetrics Observability Blog
Fred Navruzov
- Adam Yates
- Aditya Kulkarni
- Adrian Bridgwater
- Agustín Gallego
- Alex Woodie
- Alexander Marshalov
- Aliaksandr Valialkin
- Aman Agarwal
- Antony Savvas
- Artem Navoiev
- Bill Tanner
- Cer6erus
- Claudio Masolo
- David Marshall
- Denys Holius
- Derek Foster
- Diana Todea
- Dima Lazerka
- Dmytro Kozlov
- Emily Foster
- Fintech Herald
- Fred Navruzov
- Gary Flood
- Gonzalo García Labat
- Ivan Yatskevich
- Jaime Hampton
- Jan Sokol
- Jason Bloomberg
- Jason English
- Jean-Jerome Schmidt-Soisson
- Jesús Espino
- Joab Jackson
- John Seekins
- Jose Gomez-Selles
- Julien Menan
- Karan Virdi
- Laveesh Kocher
- Leigh McGowran
- Lindsay Clark
- Marc Sherwood
- Mark Baker
- Martijn Van Best
- Martin Veitch
- Mathias Palmersheim
- Michelle Sebek
- Nick Gibson
- Nikolay Khramchikhin
- Pablo Fernandez
- Phuong Le
- Rafal Szypulka
- Renato Losio
- Richard Speed
- Roman Khavronenko
- Vadim Alekseev
- Vadim Rutkovsky
- Yurii Kravets
- Zakhar Bessarab
- Zhu Jiekun
No matching authors found.
Category
- Benchmark
- Community
- Company News
- Customer Stories
- Developer Experience
- Distributed Tracing
- Events
- Go
- Go @ VictoriaMetrics
- High Cardinality
- Kubernetes
- Monitoring
- Observability
- Open Source Tech
- OpenTelemetry
- OTLP
- Performance
- PostgreSQL
- Product News
- Tech Talk
- Time Series Database
- VictoriaLogs
- VictoriaMetrics
- VictoriaTraces
No matching categories found.
Filter: Fred Navruzov
Blog posts
What's new in VictoriaMetrics Anomaly Detection (Q2 2026)
The Q2 2026 vmanomaly update introduces Temporal Envelope, a redesigned UI, faster online-model execution, and an AI-assisted workflow that turns natural-language monitoring goals into tested configurations and alerting rules.
What's new in VictoriaMetrics Anomaly Detection (Q1 2026)
Q1 2026 brought incremental but important updates to VictoriaMetrics Anomaly Detection: UI improvements, AI assistance inside the UI, a public traces playground, new false-positive reduction controls, and continued resource optimizations.
What’s new in VictoriaMetrics Anomaly Detection (2025)
VictoriaMetrics Anomaly Detection has had a productive year with lots of user feedback that has had a major impact on product development. We’ve added improvements across the board: in core functionality, simplicity, performance, visualisation and AI integration. In addition to bug fixes and speedups, below is a list of what was accomplished in 2025.
How a US Software Provider Improved Traffic Alerting with VictoriaMetrics Anomaly Detection
VictoriaMetrics Anomaly Detection enables reliable alerting for highly variable, multi-domain traffic without relying on static thresholds. In this case study, fine-tuned models, backtesting, and clear visualization helped reduce alert noise, improve confidence in anomaly detection, and lower operational overhead.
VictoriaMetrics Anomaly Detection: What's New in Q3 2024?
Explore the latest improvements in VictoriaMetrics Anomaly Detection (vmanomaly), including optimizations, online models, multitenantcy and mTLS support.
VictoriaMetrics Anomaly Detection: What's New in H1 2024?
Explore the latest improvements in VictoriaMetrics Anomaly Detection (vmanomaly), including presets, new models, enhanced tuning, and better resource management
Anomaly Detection for Time Series Data: Techniques and Models
This blog post series centers on Anomaly Detection (AD) and Root Cause Analysis (RCA) within time-series data. In Chapter 3, we delve into a variety of advanced anomaly detection techniques, encompassing supervised, semi-supervised, and unsupervised approaches, each tailored to different data scenarios and challenges in time-series analysis.
Anomaly Detection for Time Series Data: Anomaly Types
This blog post series centers on Anomaly Detection (AD) and Root Cause Analysis (RCA) within time-series data. In this second part, we explore the distinct anomaly types inherent to time-series and offer insights on how to tackle them effectively.
Anomaly Detection for Time Series Data: An Introduction
This blog post series focuses on Anomaly Detection (AD) and Root Cause Analysis (RCA) within the context of time-series data. The inaugural chapter lays the groundwork by introducing the role of AD in end-to-end observability systems, discussing domain-specific terminology, and addressing the challenges inherent to the time-series nature of the data.