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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
Quickly set up a virtual machine on Google Cloud for your deep learning project using the Deep Learning VM Image, which simplifies the process of launching a VM with essential AI frameworks on Google Compute Engine. This solution allows you to initiate Compute Engine instances that come equipped with popular libraries such as TensorFlow, PyTorch, and scikit-learn, eliminating concerns over software compatibility. Additionally, you have the flexibility to incorporate Cloud GPU and Cloud TPU support effortlessly. The Deep Learning VM Image is designed to support both the latest and most widely used machine learning frameworks, ensuring you have access to cutting-edge tools like TensorFlow and PyTorch. To enhance the speed of your model training and deployment, these images are optimized with the latest NVIDIA® CUDA-X AI libraries and drivers, as well as the Intel® Math Kernel Library. By using this service, you can hit the ground running with all necessary frameworks, libraries, and drivers pre-installed and validated for compatibility. Furthermore, the Deep Learning VM Image provides a smooth notebook experience through its integrated support for JupyterLab, facilitating an efficient workflow for your data science tasks. This combination of features makes it an ideal solution for both beginners and experienced practitioners in the field of machine learning.
API Access
Has API
API Access
Has API
Integrations
Google Cloud Platform
TensorFlow
Amazon Web Services (AWS)
Chainer
Google Cloud TPU
Google Compute Engine
JupyterLab
MXNet
Microsoft Azure
NVIDIA DRIVE
Integrations
Google Cloud Platform
TensorFlow
Amazon Web Services (AWS)
Chainer
Google Cloud TPU
Google Compute Engine
JupyterLab
MXNet
Microsoft Azure
NVIDIA DRIVE
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
Founded
1998
Country
United States
Website
cloud.google.com/deep-learning-vm
Product Features
Deep Learning
Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization
Product Features
Deep Learning
Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization