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Description

Develop and implement deep learning models tailored to specific requirements. Our streamlined machine learning process empowers clients to design robust AI solutions using our top-tier models, customized to address their unique challenges effectively. Digital platforms can efficiently generate models that align with their specific guidelines and demands. Construct large language models for niche applications, including customer service and technical support chatbots. Additionally, develop image classification models to enhance the comprehension of image collections, facilitating improved search, organization, and various other applications, ultimately leading to more efficient processes and enhanced user experiences.

Description

You can develop on your laptop, then scale the same Python code elastically across hundreds or GPUs on any cloud. Ray converts existing Python concepts into the distributed setting, so any serial application can be easily parallelized with little code changes. With a strong ecosystem distributed libraries, scale compute-heavy machine learning workloads such as model serving, deep learning, and hyperparameter tuning. Scale existing workloads (e.g. Pytorch on Ray is easy to scale by using integrations. Ray Tune and Ray Serve native Ray libraries make it easier to scale the most complex machine learning workloads like hyperparameter tuning, deep learning models training, reinforcement learning, and training deep learning models. In just 10 lines of code, you can get started with distributed hyperparameter tune. Creating distributed apps is hard. Ray is an expert in distributed execution.

API Access

Has API No 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

Amazon EC2 Trn2 Instances No 
Amazon EKS No 
Amazon SageMaker No 
Amazon Web Services (AWS) No 
Anyscale No 
Apache Airflow No 
Azure Kubernetes Service (AKS) No 
Dask No 
Databricks No 
Feast No 
Google Cloud Platform No 
Google Kubernetes Engine (GKE) No 
Hive Moderation Yes 
MLflow No 
PyTorch No 
Python No 
Snowflake No 
TensorFlow No 
Union Cloud No 
io.net No 

Integrations

Amazon EC2 Trn2 Instances Yes 
Amazon EKS Yes 
Amazon SageMaker Yes 
Amazon Web Services (AWS) Yes 
Anyscale Yes 
Apache Airflow Yes 
Azure Kubernetes Service (AKS) Yes 
Dask Yes 
Databricks Yes 
Feast Yes 
Google Cloud Platform Yes 
Google Kubernetes Engine (GKE) Yes 
Hive Moderation No 
MLflow Yes 
PyTorch Yes 
Python Yes 
Snowflake Yes 
TensorFlow Yes 
Union Cloud Yes 
io.net Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

Free
Open source. Consumption-based.
Free Trial Yes 
Free Version Yes 

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 Yes 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac Yes 
Linux Yes 
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 No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) Yes 
In Person Yes 

Vendor Details

Company Name

Hive

Founded

2013

Country

United States

Website

thehive.ai/solutions/auto-ml

Vendor Details

Company Name

Anyscale

Founded

2019

Country

United States

Website

ray.io

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 

Machine Learning

Deep Learning No 
ML Algorithm Library No 
Model Training No 
Natural Language Processing (NLP) No 
Predictive Modeling No 
Statistical / Mathematical Tools No 
Templates No 
Visualization No 

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 

Machine Learning

Deep Learning No 
ML Algorithm Library No 
Model Training No 
Natural Language Processing (NLP) No 
Predictive Modeling No 
Statistical / Mathematical Tools No 
Templates No 
Visualization No 

Alternatives

Alternatives