Accelerate your AI journey. Join our AI Community!

Publication

Latest

Top 5 Free Cloud IDE For Data Science 2021

Author(s): Abid Ali Awan

Data Science

Top 5 Free Cloud IDEs For Data Science 2021

Jumpstart your data science career with top free IDE with a built-in environment, CPU, Storage, and python packages. Learn new skills, create a project, and share it with others.

Top Ten Cloud IDE for Data Science
Image by Author | Elements by rawpixel.com

Cloud IDE

An IDE, or Integrated Development Environment, is a code editor with extra features to boost performance. IDEs increase programmer productivity by providing a one-stop solution to write, test, debug, and build solutions. Codecademy.

Cloud IDEs remove the hassle of creating an environment before you actually start coding, such as installing IDE, collaboration, storage, and computing power. Modern Cud IDEA is built on Jupyter Notebooks, and they provide everything for you to write and deploy your work. Online IDEs allow building, testing, and reviewing projects in the cloud Slant.

Jupyter notebooks are arguably the most widely used tool in data science. The JetBrains Datalore Blog. Jupyter notebook is a web-based interactive computational environment, which comes with an input/output cell to execute a snippet of code. you can also write documentation in the form of markdown within your notebook, just like you are drafting a technical article.

1. Kaggle

Kaggle notebook Indian Women Suffereing
Author’s Kaggle Notebook | Kaggle.com

Kaggle, a subsidiary of Google LLC, is an online community of data science enthusiasts, but it’s more than that. You can publish your dataset and notebook. Kaggle allows users to participate in a data science competition and win prizes, and they also offer cloud IDE (kernel) for free so that users can share and discuss improving machine learning models. Kaggle — Introduction

Kaggle platform has evolved into the complete ecosystem for data scientists where users can run notebooks on CPU, GPU, and TPU. You can also run python or R scripts. In short, if you are thinking of starting your career in a sub-field of data science, please start with the Kaggle platform, as you have the largest communities of Data scientists who are sharing their ideas on a daily basis. Kaggle comes with a one-stop shop for Data scientists and machine learning practitioners to compete and grow together as a community. For more Information Kaggle — Wikipedia or Visit Kaggle website

What will you get when you sign up?

  • Friendly community
  • Free CPU
  • Live collaborative coding
  • 5GB storage per project
  • Custom Environment
  • Publishing Platform
  • Database integration
  • New cell types
  • Schedule Run
  • Project History/snapshot

My experience with Kaggle was amazing, and within no time, you start to understand how you can run your code or fork other works. The IDE is super-fast and clean to work around. The community is super friendly, and multiple ongoing competitions keep this platform interesting as Machine learning engineers all over the world strive for glory and a huge price pool. You can also learn few courses and get certificates by completing the course. You can also throw your public or private Data science competitions for your class or masses. This platform comes with all essential tools that can prepare you for real-world projects as you are working on real-world data, and the competitive environment makes you better as you spend more time on it.

2. Deepnote

Author’s Deepnote Notebook | Deepnote.com

Deepnote is a data science notebook built for teams and live collaboration. It allows you to create, build and share data science projects. The interactive user interface makes it an attractive option for beginners to coding on python, R and Julia. The platform allows you to focus on coding and building data science solutions and leave the rest to Deepnote IDE Jakub Jurovych.

The Deepnote is a new contender in this industry and quickly rising to the top. It has an active and helping community, and even the CEO of the company is interacting with users. The platform comes with live support, and you can suggest new features or bug report, which they quickly add it into their development pipeline. The Deepnote kernel comes with all essential libraries, a schedule notebook, modern UI, and a free team plan. You can add up to three members for free to your team and start working on projects with a live collaborative tool. When you are finished with your work, you can either publish your notebook as an article or as a WebApp. For more information, visit Deepnote Website.

What will you get when you sign up?

  • Friendly community
  • Free CPU
  • Live collaborative coding
  • Live support
  • 5GB storage per project
  • Custom Environment
  • Publishing Platform
  • Database integration
  • New cell types
  • Schedule Run
  • Project History/snapshot

My experience with Deepnote was love at first sight. It had everything I wanted, and it had a fresh look. The only thing that made me put it in second place is that they offer paid GPU, and they don’t have any competitive or learning platform. We can say they are still new to this world, and with time, they might add new features. I have nearly created 58 projects on Deepnote and published more than 30+ articles which you can access from deepnote.com/@abid. If I start a new project, I start with Deepnote and then move to another platform if I need GPU or TPU. I will recommend anyone who is looking to learn data science and machine learning to start with Deepnote, and I am not sponsored or paid by Deepnote.

3. Google Colab

Sample Google Colab Notebook | colab.research.google.com

The Google Colab is a quick solution to your deep learning problems. With Colab, you can add a dataset and train your neural net using Google’s cloud servers GPU or TPU and evaluate all in your browser. The platform allows you to share your code and integrate google services to improve your workstation Colab

The Colab has far fewer features than Deepnote and Kaggle. It has even lesser features than Gradient and Datalore. The reason Colab is 3rd on my ranking is free GPU and CPU and fast loading time. You don’t have to signup, your google account will work fine, and you can integrate your google drive to save and load your data. The UI is quite simple, and most of the deep learning projects have a link to Google Colab, which makes it quite popular among Data scientists. The Machine learning practitioners experiment and train their model and then deploy it for production. With just one click, your machine is ready to perform a mammoth task, and its simplicity and huge following make it into the top 3. For more information, visit Google Colab

What will you get when you sign up?

  • Free GPU & TPU
  • Google Integration
  • Storage
  • Complete python environment
  • Notebook sharing

I have a love and hate relationship with Colab. Even though it’s free, it comes with tons of performance issues, and if you are not interacting with cells, it will automatically shut down the process. Even then, my third option is Colab for running my projects, as I get fast CPU and GPU within seconds. You need to learn to handle Google Colab features and how to avoid machines automatically shut down while you are training model. I will suggest keeping Colab as your experimental buddy as it will frustrate you from time to time. When I am exploring projects on GitHub, it’s easy to run it on Colab as there is seamless integration between GitHub and Colab platform. You can run any Jupiter notebook file on it with few clicks.

4. Gradient

FastAI Gradient Notebook | gradient.paperspace.com

Focus on building models, not managing your environment. Launch a notebook with preconfigured python environment which includes, machine learning frameworks, python libraries, and drivers that your need to run any deep learning models. all the frameworks, libraries, and drivers you need for deep learning. Gradient comes with fully customized containers that you can load with pre-install libraries and datasets that will get you started within few seconds. Install any custom dependencies compatible with Jupyter. Apart from Cloud IDE, you get a complete Machine Learning ecosystem to deploy and test your model into production. You can transform your Ideas directly from notebooks into production with a Paperspace integrated platform. Paper space.

Gradient comes with free CPU and GPU, but the GPU is shared, and most of the time, it’s unavailable for multiple reasons. With Gradient, you get complete MLOps which means you can experiment, load data, deploy your model, and monitor the performance. When you start your project, it will ask you to select from the custom environment and then select the machine to start the instance. It comes with a completely new UI with a modern design, and it offers a lot of benefits once you get used to it. Gradient comes with Version history, and you can share your notebook with your teammates. For more information, read the documentation or visit Gradient.

What will you get when you sign up?

  • Deployment Platform
  • Free GPU and CPU
  • Public Datasets
  • Easy sharing
  • workflow
  • Custom Environment
  • Community Support
  • Version History
  • CLI

My experience with gradient was quite positive as I started using it due to the Jermy Howard FastAI course. It has everything for a beginner, but sometimes it can frustrate you as there are too many options to choose and sometimes the free CPU or GPU that you receive are low-end machines. Overall, I like how it provides a complete ecosystem for MLOPs, so if you are starting a career in machine learning, I will suggest you start with Gradient. The reason I kept it at 4th position is due to unpredictable machine type, paid collaboration tool, ipywidgets are disabled, and it’s purely focused on machine learning. Gradient has potential if they keep adding new features such as schedule run. I think with time, I might replace it with Google Colab as it has the potential to take over old guards.

5. Datalore

Author’s Datalore Notebook | datalore.jetbrains.com

Datalore is a product of JetBrains, which comes with a powerful online environment for Jupyter notebooks that enables you to edit, execute, and share your code more productively Datalore (jetbrains.com). The Datalore requires no setup, and immediately you are into an interactive environment to load your data, train your machine learning model and visualize the performance all in one place. You can also share your notebook with your teammates and collaborate on open-source projects.

Datalore comes with preloaded python libraries with free CPU and GPU, but the reason I have kept it on the 5th spot is that it only provides limited hours of CPU and GPU. In short, it offers you team collaboration, and you can share your notebook with other users. It has an active community that is always reporting bugs and giving suggestions for product improvement. The editor provides coding assistance, code completion, and automatic quick fixes. You can switch between reactive mode, which converts Jupiter notebook into the article. Overall if you are looking for other options and you love JetBrains products, you will also love Datalore. For more information, visit Datalore

What will you get when you sign up?

  • 120-hour CPU
  • 10 hours GPU
  • 10 GB of cloud storage + S3 bucket support
  • Active Community
  • Shared Workplace
  • version history

Initially, when I started using Datalore back in 2020, it was quite bad, as some of the python libraries did not work, and the machine was low-end to even consider it a viable option. I hated the UI, and I thought how such a big and renowned company could launch such a low-end product. But with time, things started to change, and they have improved the machine performance and added a new feature; they are now offering free limited GPU. With that, I think Datalore has the potential to become an industry leader, but it still lacks features such as version history, database integrations, custom environment, and publishing platform.

Conclusion

In this article, I have covered all the bases on Top 5 Cloud IDE, and I have also shared my personal experience. The other free cloud IDEs that are promising and didn’t make it to my top five are Floyd hub, Saturn Cloud, Binder, Cocal, and Grid. These cloud IDE lack in performance, free tiers require a credit card, and lack of features make it inviable for our top list.

Image by Author | Element by freepik

If you are starting your career in Data Science or a data professional and you don’t have a good machine to work on, I will highly recommend you move from a local machine to a cloud as it provides you flexibility and a hassle-free workbench.

My final three Free Cloud IDE are Kaggle, Deepnote, and Google Colab.

Usually, I start my machine learning project on Deepnote and then move to Kaggle of Colab for better processing. The Deepnote platform provides me publishing and interactive coding platform that improves my productivity. If you are still confused about which platform you want to start with, I will suggest you start with Kaggle and then look for other options as Kaggle has everything you need to Jumpstart your career.

That’s it. I hope you like my article and don’t forget to like and share my article.


Top 5 Free Cloud IDE For Data Science 2021 was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.

Published via Towards AI

Feedback ↓