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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
Utilizing Automaton AI's ADVIT platform, you can effortlessly create, manage, and enhance high-quality training data alongside DNN models, all from a single interface. The system automatically optimizes data for each stage of the computer vision pipeline, allowing for a streamlined approach to data labeling processes and in-house data pipelines. You can efficiently handle both structured and unstructured datasets—be it video, images, or text—while employing automatic functions that prepare your data for every phase of the deep learning workflow. Once the data is accurately labeled and undergoes quality assurance, you can proceed with training your own model effectively. Deep neural network training requires careful hyperparameter tuning, including adjustments to batch size and learning rates, which are essential for maximizing model performance. Additionally, you can optimize and apply transfer learning to enhance the accuracy of your trained models. After the training phase, the model can be deployed into production seamlessly. ADVIT also supports model versioning, ensuring that model development and accuracy metrics are tracked in real-time. By leveraging a pre-trained DNN model for automatic labeling, you can further improve the overall accuracy of your models, paving the way for more robust applications in the future. This comprehensive approach to data and model management significantly enhances the efficiency of machine learning projects.
API Access
Has API
API Access
Has API
Integrations
Amazon Web Services (AWS)
Google Cloud Platform
Microsoft Azure
TensorFlow
Integrations
Amazon Web Services (AWS)
Google Cloud Platform
Microsoft Azure
TensorFlow
Pricing Details
$200 per month
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Auger.AI
Founded
2019
Country
United States
Website
auger.ai/
Vendor Details
Company Name
Automaton AI
Founded
2019
Country
India
Website
automatonai.com
Product Features
Deep Learning
Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization
Product Features
Data Labeling
Human-in-the-loop
Labeling Automation
Labeling Quality
Performance Tracking
Polygon, Rectangle, Line, Point
SDK
Supports Audio Files
Task Management
Team Collaboration
Training Data Management
Deep Learning
Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization
Machine Learning
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization