Clouderizer – Affordable Machine Learning Platform

Cloud IDE for Data Scientists

✓ Setup, train and serve your deep learning projects
✓ Save time and costs
✓ Increase productivity & collaboration

Why Clouderizer

Ease Of Use

  • Recreate state-of-the-art ML models in few clicks
  • Setup your cloud GPUs without worrying about DevOps
  • Github & Kaggle Datasets integration

Save Time

  • Clone and iterate existing projects in a few seconds
  • Automate DevOps and model training
  • Out of box serving for your trained models

Save Costs

  • Use AWS spot & GCP pre-emptible instances with data persistence​
  • Bring Your Own Cloud (BYOC) and use your existing coupons and credits from cloud providers.

Who needs Clouderizer

  • Aspiring data scientist & startups, looking to explore machine learning.
  • High infrastructure costs & complex DevOps is a barrier to entry!
  • Faculty spending too much time on DevOps of data science homework & assignment!
  • Looking for low cost GPUs for students
  • Concerned with sharing private datasets with 3rd party solutions
  • Enable data scientists to run experiments on cloud without worrying about DevOps


Forget DevOps.
Focus on Machine Learning.

  • Pre-installed ML frameworks and tools like Tensorflow, PyTorch, Keras, JupyterLab, Tensorboard
  • Run projects in docker container optimized for GPU using nvidia drivers
  • Seamless integration with Git, Kaggle to setup workspace code and datasets
  • Add scripts to automate custom dependency setup

Run projects locally, on cloud or both

  • Run project on cloud of your choice.
  • Workspace persistence through real time sync of code and data with Google Drive.
  • Option to run project locally on any Ubuntu machine as well

Data Security

  • Your code and data remains private to you on your own cloud infrastructure. Nothing is stored on Clouderizer servers
  • Ideal for working on sensitive and confidential datasets
  • Workspace is backed up on your Google Drive or S3
  • All communications are encrypted using SSL/TLS

Reduce cloud bills

  • Effortlessly run your projects on EC2 Spot or GCP Pre-emptible instances, with a single click
  • Use credits and coupons codes you already have with EC2 and GCP
  • Run your Deep Learning projects at almost same cost as underlying cloud machines.

Try out a project from our community now

Practical Deep Learning for Coders, v3, 2019 course by Jeremy Howard.


Colourise the black & white images, based on scenario interpretation powered by GANs.


Recreate state-of-art object detection using Facebook Research Lab's Detectron.


Fast Style Transfer using PyTorch. Quickly train your models and demo using Clouderizer Serve.

Get help

Public forum for discussion around our product. Get help from other users.

Product Documentation to get you started and fill in with advanced features.

How To videos around core features

We used Clouderizer while teaching the Deep Learning course for graduate and undergraduate students, as one of the options to do programming homeworks and assignments at Georgia Tech.

With Clouderizer, students who used it found it easy to use, were able to get started with cloud GPU setup and running in few minutes, without hassle and extra work of setting up the environment, installing different packages, versions and dependencies.
I would recommend  Clouderizer to any university or company which runs employee / student training related to Machine Learning and is looking for a Cloud interface vendor / provider.

Sreenivasan AC
Graduate Teaching Assistant, Deep Learning, Georgia Institute of Technology

Clouderizer has saved me hours of low-level technical fiddling. I love how I can choose with one click which cloud service to spin up a machine on and how everything seamlessly backs up to my Google Drive. Customer service is excellent too - I had a connectivity issue and within ten minutes I had a video call and the problem was resolved. It’s well worth the money and highly recommended!

Anthony Holmes
CEO, Holmes Consulting

I have been using Clouderizer for over 3 months, its great to start because it lets me connect to Colab and GCP, perfect for starting out and you don’t want to fight with SSH

Jeremy Easterbrook
CEO Index

Check out Now

Ready to build and deploy a model with ML?