Ray

Ray

Anyscale
+
+

Related Products

  • Fraud.net
    56 Ratings
    Visit Website
  • Vertex AI
    944 Ratings
    Visit Website
  • Dataiku
    203 Ratings
    Visit Website
  • Qloo
    23 Ratings
    Visit Website
  • RunPod
    205 Ratings
    Visit Website
  • XpertCoding
    42 Ratings
    Visit Website
  • PackageX OCR Scanning
    46 Ratings
    Visit Website
  • QueryPal
    2 Ratings
    Visit Website
  • QEval
    30 Ratings
    Visit Website
  • SBS Financing Platform
    3 Ratings
    Visit Website

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
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Companies interested in a solution for ensuring machine learning model accuracy

Audience

ML and AI Engineers, Software Developers

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Pricing

$200 per month
Free Version
Free Trial

Pricing

Free
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Auger.AI
Founded: 2019
United States
auger.ai/

Company Information

Anyscale
Founded: 2019
United States
ray.io

Alternatives

PowerAI

PowerAI

Buzz Solutions

Alternatives

SparkPredict

SparkPredict

SparkCognition
Keepsake

Keepsake

Replicate

Categories

Categories

Integrations

Amazon Web Services (AWS)
Google Cloud Platform
TensorFlow
Amazon EC2 Trn2 Instances
Amazon EKS
Anyscale
Apache Airflow
Azure Kubernetes Service (AKS)
Databricks Data Intelligence Platform
Feast
Flyte
Google Kubernetes Engine (GKE)
Kubernetes
LanceDB
MLflow
PyTorch
Python
Snowflake
Union Cloud
io.net

Integrations

Amazon Web Services (AWS)
Google Cloud Platform
TensorFlow
Amazon EC2 Trn2 Instances
Amazon EKS
Anyscale
Apache Airflow
Azure Kubernetes Service (AKS)
Databricks Data Intelligence Platform
Feast
Flyte
Google Kubernetes Engine (GKE)
Kubernetes
LanceDB
MLflow
PyTorch
Python
Snowflake
Union Cloud
io.net
Claim Auger.AI and update features and information
Claim Auger.AI and update features and information
Claim Ray and update features and information
Claim Ray and update features and information