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ease 0 out of 5 stars
features 0 out of 5 stars
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Average Ratings 0 Ratings

Total 0 out of 5 stars
ease 0 out of 5 stars
features 0 out of 5 stars
design 0 out of 5 stars
support 0 out of 5 stars

No User Reviews. Be the first to provide a review:

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Description

Declarative machine learning systems offer an ideal combination of flexibility and ease of use, facilitating the rapid implementation of cutting-edge models. Users concentrate on defining the “what” while the system autonomously determines the “how.” Though you can start with intelligent defaults, you have the freedom to adjust parameters extensively, even diving into code if necessary. Our team has been at the forefront of developing declarative machine learning systems in the industry, exemplified by Ludwig at Uber and Overton at Apple. Enjoy a selection of prebuilt data connectors designed for seamless compatibility with your databases, data warehouses, lakehouses, and object storage solutions. This approach allows you to train advanced deep learning models without the hassle of infrastructure management. Automated Machine Learning achieves a perfect equilibrium between flexibility and control, all while maintaining a declarative structure. By adopting this declarative method, you can finally train and deploy models at the speed you desire, enhancing productivity and innovation in your projects. The ease of use encourages experimentation, making it easier to refine models based on your specific needs.

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 Yes 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

Python Yes 
Snowflake Yes 
Amazon EC2 Trn2 Instances No 
Amazon EKS No 
Amazon Redshift Yes 
Apache Airflow No 
Apache Hive Yes 
Dask No 
Databricks No 
Feast No 
Flyte No 
Kubernetes No 
LanceDB No 
LiteLLM Yes 
MLflow No 
Opik Yes 
PostgreSQL Yes 
PyTorch No 
TensorFlow No 
io.net No 

Integrations

Python Yes 
Snowflake Yes 
Amazon EC2 Trn2 Instances Yes 
Amazon EKS Yes 
Amazon Redshift No 
Apache Airflow Yes 
Apache Hive No 
Dask Yes 
Databricks Yes 
Feast Yes 
Flyte Yes 
Kubernetes Yes 
LanceDB Yes 
LiteLLM No 
MLflow Yes 
Opik No 
PostgreSQL No 
PyTorch Yes 
TensorFlow Yes 
io.net Yes 

Pricing Details

Pay-as-you-go pricing
Free Trial Yes 
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) Yes 
In Person No 

Types of Training

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

Vendor Details

Company Name

Predibase

Country

United States

Website

predibase.com

Vendor Details

Company Name

Anyscale

Founded

2019

Country

United States

Website

ray.io

Product Features

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 

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