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Average Ratings 0 Ratings
Description
Minimize false positives and leverage machine learning (ML) to effectively identify anomalies in business performance indicators. Investigate the underlying causes of these anomalies by clustering similar outliers together for analysis. Provide a summary of these root causes and prioritize them based on their impact. Ensure a smooth integration with AWS databases, storage services, and external SaaS platforms for comprehensive metrics monitoring and anomaly detection. Set up automated alerts and responses tailored to the detection of anomalies. Utilize Lookout for Metrics, which employs ML to both discover and analyze anomalies in business and operational datasets. The challenge of recognizing unexpected anomalies is compounded by the limitations of traditional manual methods that are prone to errors. Lookout for Metrics simplifies the detection and diagnosis of data inconsistencies without requiring any expertise in artificial intelligence (AI). Monitor irregular fluctuations in subscriptions, conversion rates, and revenue to remain vigilant about sudden market shifts, ultimately enhancing strategic decision-making capabilities. By adopting these advanced techniques, businesses can improve their overall performance management and response strategies.
Description
LotusEye offers a cloud-based service for AI-driven anomaly detection that autonomously acquires knowledge of standard behavior from numerical or sensor data provided in CSV format and consistently computes anomaly scores to identify irregularities that could signify faults or unforeseen activities, delivering notifications and visual analytics without necessitating any machine learning expertise from users. The service accommodates both wide-format CSV files, where every row corresponds to sensor readings at specific timestamps, and long-format CSV files that include timestamp, sensor name, and value columns, allowing users to upload their data either through a simple drag-and-drop interface or via an API for automated processing on a scheduled basis. Once an AI model is trained using data from normal operations, users can then input test data to obtain calculated anomaly scores and view these results on dashboards featuring time-series graphs, threshold markers, and filtering options, which assist teams in identifying unusual trends and probing potential concerns swiftly. This streamlined process enhances operational efficiency and empowers teams to act on insights generated by the platform.
API Access
Has API
Yes
API Access
Has API
Yes
Integrations
AWS Lambda
Yes
Amazon CloudWatch
Yes
Amazon Redshift
Yes
Amazon S3
Yes
Amazon Simple Notification Service (SNS)
Yes
Google Sheets
No
Microsoft Excel
No
Integrations
AWS Lambda
No
Amazon CloudWatch
No
Amazon Redshift
No
Amazon S3
No
Amazon Simple Notification Service (SNS)
No
Google Sheets
Yes
Microsoft Excel
Yes
Pricing Details
No price information available.
Free Trial
Yes
Free Version
No
Pricing Details
$13 per month
Free Trial
No
Free Version
Yes
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
Yes
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
Amazon
Founded
1994
Country
United States
Website
aws.amazon.com/lookout-for-metrics/
Vendor Details
Company Name
LotusEye
Country
Japan
Website
lotuseye.co.jp/
Product Features
Machine Learning
Deep Learning
No
ML Algorithm Library
No
Model Training
No
Natural Language Processing (NLP)
No
Predictive Modeling
No
Statistical / Mathematical Tools
No
Templates
No
Visualization
No