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What is VictoriaMetrics Anomaly Detection?

VictoriaMetrics Anomaly Detection is a continuous monitoring service that analyzes data within VictoriaMetrics to identify real-time unexpected variations in data patterns. This innovative solution employs customizable machine learning models to effectively pinpoint anomalies. As a vital component of our Enterprise offering, VictoriaMetrics Anomaly Detection serves as an essential resource for navigating the intricacies of system monitoring in an ever-evolving landscape. It significantly aids Site Reliability Engineers (SREs), DevOps professionals, and other teams by automating the intricate process of detecting unusual behavior in time series data. Unlike traditional threshold-based alerting systems, it leverages machine learning techniques to uncover anomalies, thereby reducing the occurrence of false positives and alleviating alert fatigue. The implementation of unified anomaly scores and streamlined alerting processes enables teams to swiftly recognize and resolve potential issues, ultimately enhancing the reliability of their systems. By adopting this advanced anomaly detection service, organizations can ensure more proactive and efficient management of their data-driven operations.

What is Auger.AI?

Auger.AI offers a robust solution aimed at ensuring the accuracy of machine learning models. Our Machine Learning Review and Monitoring (MLRAM) tool plays a crucial role in keeping your models' precision consistent over time. It also evaluates the return on investment for your predictive analytics efforts! MLRAM's compatibility with any machine learning technology stack enhances its flexibility and adaptability. Failing to regularly assess the accuracy of your machine learning system lifecycle could lead to financial losses due to inaccurate predictions. Additionally, the ongoing requirement for model retraining can be expensive and may not resolve issues related to concept drift. MLRAM provides substantial advantages for both data scientists and business stakeholders, including features such as accuracy visualization graphs, performance alerts, anomaly detection, and automated optimized retraining. The process of integrating your predictive model with MLRAM is incredibly straightforward, requiring just a single line of code. We also offer a free one-month trial of MLRAM for qualifying users. With Auger.AI, you can leverage the most reliable AutoML platform available, empowering your organization to enhance its predictive capabilities to the fullest. This comprehensive approach ensures that your machine learning initiatives remain efficient and cost-effective over time.

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Integrations Supported

Amazon Web Services (AWS)
Google Cloud Platform
Microsoft Azure
TensorFlow
VictoriaMetrics Enterprise

Integrations Supported

Amazon Web Services (AWS)
Google Cloud Platform
Microsoft Azure
TensorFlow
VictoriaMetrics Enterprise

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

$200 per month
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

VictoriaMetrics

Date Founded

2018

Company Location

United States

Company Website

victoriametrics.com/products/enterprise/anomaly-detection/

Company Facts

Organization Name

Auger.AI

Date Founded

2019

Company Location

United States

Company Website

auger.ai/

Categories and Features

IT Infrastructure Monitoring

Alerts / Notifications
Application Monitoring
Bandwidth Monitoring
Capacity Planning
Configuration Change Management
Data Movement Monitoring
Health Monitoring
Multi-Platform Support
Performance Monitoring
Point-in-Time Visibility
Reporting / Analytics
Virtual Machine Monitoring

Categories and Features

Deep Learning

Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization

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