Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
Auger.AI delivers the most comprehensive solution for maintaining the accuracy of machine learning models. Our MLRAM tool (Machine Learning Review and Monitoring) guarantees that your models maintain their accuracy over time. It even assesses the return on investment for your predictive models! MLRAM is compatible with any machine learning technology stack. If your ML system lifecycle lacks ongoing measurement of model accuracy, you could be forfeiting profits due to erroneous predictions. Additionally, frequently retraining models can be costly and may not resolve issues caused by concept drift. MLRAM offers significant benefits for both data scientists and business professionals, featuring tools such as accuracy visualization graphs, performance and accuracy notifications, anomaly detection, and automated optimized retraining. Integrating your predictive model with MLRAM requires just a single line of code, making the process seamless. We also provide a complimentary one-month trial of MLRAM for eligible users. Ultimately, Auger.AI stands out as the most precise AutoML platform available, ensuring that your machine learning initiatives are both effective and efficient.
Description
Safeguard business service-level agreements by utilizing dashboards that enable monitoring of service health, troubleshooting alerts, and conducting root cause analyses. Enhance mean time to resolution (MTTR) through real-time event correlation, automated incident prioritization, and seamless integrations with IT service management (ITSM) and orchestration tools. Leverage advanced analytics, including anomaly detection, adaptive thresholding, and predictive health scoring, to keep an eye on key performance indicators (KPIs) and proactively avert potential issues up to 30 minutes ahead of time. Track performance in alignment with business operations through ready-made dashboards that not only display service health but also visually link services to their underlying infrastructure. Employ side-by-side comparisons of various services while correlating metrics over time to uncover root causes effectively. Utilize machine learning algorithms alongside historical service health scores to forecast future incidents accurately. Implement adaptive thresholding and anomaly detection techniques that automatically refine rules based on previously observed behaviors, ensuring that your alerts remain relevant and timely. This continuous monitoring and adjustment of thresholds can significantly enhance operational efficiency.
API Access
Has API
Yes
API Access
Has API
No
Integrations
AXYVOR
No
Amazon Web Services (AWS)
Yes
BlueCat Gateway
No
CloudApper iPaaS
No
CloudFabrix
No
Evolven
No
Google Cloud Platform
Yes
Microsoft Azure
Yes
SAP Store
No
Splunk User Behavior Analytics
No
Integrations
AXYVOR
Yes
Amazon Web Services (AWS)
No
BlueCat Gateway
Yes
CloudApper iPaaS
Yes
CloudFabrix
Yes
Evolven
Yes
Google Cloud Platform
No
Microsoft Azure
No
SAP Store
Yes
Splunk User Behavior Analytics
Yes
Pricing Details
$200 per month
Free Trial
Yes
Free Version
No
Pricing Details
No price information available.
Free Trial
No
Free Version
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
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)
No
Online Support
Yes
Customer Support
Business Hours
Yes
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
Yes
In Person
No
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
Auger.AI
Founded
2019
Country
United States
Website
auger.ai/
Vendor Details
Company Name
Cisco
Founded
1984
Country
United States
Website
www.splunk.com/en_us/products/it-service-intelligence.html
Product Features
Deep Learning
Convolutional Neural Networks
No
Document Classification
No
Image Segmentation
No
ML Algorithm Library
No
Model Training
No
Neural Network Modeling
No
Self-Learning
No
Visualization
No
Product Features
IT Alerting
Alert Noise Reduction
No
Alert Routing
No
Dynamic Notifications
No
Enriched Incident Context
No
Escalation Policies
No
Incident History Audit
No
Multi-User Alerting
No
Multiple Alert Types
No
On-Call Management
No
Rich HTML Email Notifications
No
IT Service
Contract Management
No
IT Asset Management
No
Incident Management
No
Knowledge Management
No
Release Management
No
Self Service Portal
No
Service Catalog
No
Service Reporting
No
Ticket Management
No