Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
Datoin eliminates the challenges associated with entering the realm of Machine Learning by utilizing a user-friendly graphical interface and a no-code methodology. This innovative platform is crafted to swiftly bring your ideas to fruition. A key strategy for reducing expenses is to make the most of resources through repeated use. Datoin’s Block Superstore features an extensive array of components, including enterprise software connectors, ETL tools, machine learning frameworks, NLP libraries, cloud service integrations, and various SaaS APIs. The advantage of using Datoin lies in its continuous expansion; as we explore new use cases, additional blocks are consistently incorporated into the store. The availability of pre-built machine learning models allows users to bypass the initial training phase, enabling a quick start. We are dedicated to developing blocks that address common challenges faced across different industries and functional areas. Furthermore, if you have any doubts regarding particular features or their effectiveness, you can easily experiment by modifying existing applications, ensuring you find the right solution for your needs. This flexibility not only enhances user confidence but also fosters innovation in problem-solving.
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
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
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
Pricing Details
No price information available.
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
Yes
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
Datoin
Founded
2016
Country
India
Website
datoin.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