RayAnyscale
|
||||||
Related Products
|
||||||
About
Auger.AI has the most complete solution for ensuring machine learning model accuracy. Our MLRAM tool (Machine Learning Review and Monitoring) ensures your models are consistently accurate. It even computes the ROI of your predictive model! MLRAM works with any machine learning technology stack. If your ML system lifecyle doesn’t include consistent measurement of model accuracy, you’re likely losing money from inaccurate predictions. And frequent retraining of models is both expensive and, if they’re experiencing concept drift, may not fix the underlying problem. MLRAM provides value to both the data scientist and business user with features like accuracy visualization graphs, performance and accuracy alerts, anomaly detection and automated optimized retraining. Hooking up your predictive model to MLRAM is just a single line of code. We offer a free one month trial of MLRAM to qualified users. Auger.AI is the most accurate AutoML platform.
|
About
Develop on your laptop and then scale the same Python code elastically across hundreds of nodes or GPUs on any cloud, with no changes. Ray translates existing Python concepts to the distributed setting, allowing any serial application to be easily parallelized with minimal code changes. Easily scale compute-heavy machine learning workloads like deep learning, model serving, and hyperparameter tuning with a strong ecosystem of distributed libraries. Scale existing workloads (for eg. Pytorch) on Ray with minimal effort by tapping into integrations. Native Ray libraries, such as Ray Tune and Ray Serve, lower the effort to scale the most compute-intensive machine learning workloads, such as hyperparameter tuning, training deep learning models, and reinforcement learning. For example, get started with distributed hyperparameter tuning in just 10 lines of code. Creating distributed apps is hard. Ray handles all aspects of distributed execution.
|
|||||
Platforms Supported
Windows
Not Supported
Mac
Not Supported
Linux
Not Supported
Cloud
Supported
On-Premises
Not Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
|
Platforms Supported
Windows
Supported
Mac
Supported
Linux
Supported
Cloud
Supported
On-Premises
Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
|
|||||
Audience
Companies interested in a solution for ensuring machine learning model accuracy
|
Audience
ML and AI Engineers, Software Developers
|
|||||
Support
Phone Support
Not Supported
24/7 Live Support
Not Supported
Online
Supported
|
Support
Phone Support
Not Supported
24/7 Live Support
Not Supported
Online
Supported
|
|||||
API
Offers API
Supported
|
API
Offers API
Supported
|
|||||
Screenshots and Videos |
Screenshots and Videos |
|||||
Pricing
$200 per month
Free Version
Not Supported
Free Trial
Supported
|
Pricing
Free
Open source. Consumption-based.
Free Version
Supported
Free Trial
Supported
|
|||||
Reviews/
|
Reviews/
|
|||||
Training
Documentation
Supported
Webinars
Supported
Live Online
Supported
In Person
Not Supported
|
Training
Documentation
Supported
Webinars
Supported
Live Online
Supported
In Person
Supported
|
|||||
Company InformationAuger.AI
Founded: 2019
United States
auger.ai/
|
Company InformationAnyscale
Founded: 2019
United States
ray.io
|
|||||
Alternatives |
Alternatives |
|||||
|
|
||||||
|
|
||||||
Categories |
Categories |
|||||
Integrations
Amazon Web Services (AWS)
Supported
Google Cloud Platform
Supported
TensorFlow
Supported
Amazon EC2 Trn2 Instances
Not Supported
Amazon EKS
Not Supported
Amazon SageMaker
Not Supported
Anyscale
Not Supported
Apache Airflow
Not Supported
Azure Kubernetes Service (AKS)
Not Supported
Dask
Not Supported
|
Integrations
Amazon Web Services (AWS)
Supported
Google Cloud Platform
Supported
TensorFlow
Supported
Amazon EC2 Trn2 Instances
Supported
Amazon EKS
Supported
Amazon SageMaker
Supported
Anyscale
Supported
Apache Airflow
Supported
Azure Kubernetes Service (AKS)
Supported
Dask
Supported
|
|||||
|
|
|