Parallel on-cloud model training

Everything done in deep learning teams, from te data gathering and data prepataion to augmentation and such are done for the purpose of training a model. Model training requires both a relatively deep knowledge of deep learning and deep knowledge of infrastructure and devops. With one of the most complete model libraries in industry, training a model has nver been this easy.

Parallel on-cloud model training

All major frameworks

Choose from a vast library of models and frameworks to train

High-end hardware

Train on the top of the line dedicated hardware for quick results

No coding required

Train advanced models with a few clicks! No coding required at all

On-cloud model training

With one of the most complete models and frameworks libraries, you can train the model of your choice without
writing even a single line of code. Training with top of the line, high end hardware designed for AI model training means that your models will be ready faster than you think.

We support most of the major Object detection, Segmentation and classification models served on PyTorch, TensorFlow and nVidia TAO.

All major frameworks

While most deep learning platforms don’t give you much of a choice when it comes to framework selection (with some outright performing auto ml with no choice at all), we have implemented the three major frameworks to choose from, Wether you want to deploy your mode on cloud or locally on windows, linux, on GPU or CPU, there is an option for you. You can also exploit the benefits of each framework beyond the deployment according to your needs.

All computer vision modes

most of the major Object detection, segmentation and classification models are served on our platform. you can analyze each model and compare it to the rest using Tensorboard to be able to choose the best model that can serve your special needs. We don’t believe in limiting our users to single models and believe that each user might find some models more aligned to their needs.

Train without coding

Some of the most prominent complexities in deep learning adoption, besides the data requirements are the complexities surrounding setting up training procedures and feeding the data to the model, analyzing the results and testing the model. These tasks often call for extensive coding and DevOps procedure to be undertaken. We have done our best to make sure that once your data is ready, you’re just a few clicks away from having functional models trained on the best hardware possible without even a single line of code.