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Average Ratings 0 Ratings

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features
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support

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Write a Review

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

For the first time, artificial intelligence collaborates seamlessly with experts in specific fields and data analysts to introduce automation tailored for the financial services sector. Enjoy the advantages of continuously enhancing outcomes with every validated document processed. Our innovative platform simplifies the document automation journey, enabling a swift transition from initial idea to live application. You can easily modify workflows and extract insights that empower renowned data scientists with essential training datasets. Moreover, you can effectively incorporate AI models into these workflows to optimize performance, as our system autonomously assesses model outputs against both test datasets and live environments. To uphold rigorous standards concerning data privacy, audit trails, and compliance with regulations such as GDPR, Cognaize can be implemented in various environments including public or private clouds as well as on-premise setups. The software development kit (SDK) provides training data to support your data science team in retraining models and seamless integration, while application programming interfaces (APIs) facilitate the incorporation of Cognaize into your existing databases or downstream applications, ensuring a comprehensive solution for your automation needs. As organizations increasingly rely on AI-driven insights, the need for robust and adaptable systems will only continue to grow.

API Access

Has API Yes 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

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

Integrations

Amazon Web Services (AWS) No 
Google Cloud Platform No 
Microsoft Azure No 
TensorFlow No 

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 No 
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 No 
Webinars No 
Live Training (Online) Yes 
In Person No 

Vendor Details

Company Name

Auger.AI

Founded

2019

Country

United States

Website

auger.ai/

Vendor Details

Company Name

Cognaize

Founded

2020

Country

United States

Website

www.cognaize.com

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 

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