Top 5 : Online Notebook (ipynb) and other cloud services.

I ) I don’t know what is a Cloud ?are they like clouds in the sky ?

>More or less (the diagram below will clear up your idea of cloud).

The practice of using a network of remote servers hosted on the Internet to store, manage, and process data, rather than a local server or a personal computer.

II )That sounds cool, but what the heck is notebook?

> Jupyter Notebook (aka notebook) is an open-source web application that allows us to create and share codes and documents.

It provides an environment, where you can document your code, run it, look at the outcome, visualize data and see the results without leaving the environment. This makes it a handy tool for performing end to end data science workflows — data cleaning, statistical modeling, building and training machine learning models, visualizing data, and many, many other uses.(find more here)

Most compelling features of Jupyter notebook :

Language of choice

The Notebook has support for over 40 programming languages, including Python, R, Julia, and Scala.

Share notebooks

Notebooks can be shared with others using email, Dropbox, GitHub and the Jupyter Notebook Viewer.

Interactive output

our code can produce rich, interactive output: HTML, images, videos, LaTeX, and custom MIME types.

Big data integration

Leverage big data tools, such as Apache Spark, from Python, R and Scala. Explore that same data with pandas, scikit-learn, ggplot2, TensorFlow.

III ) What can I really do with jupyter notebook?

>There are tons of things u can do in jupyter notebook. There is a whole page dedicated for it. Pls find it here.

Now, Let’s get started !!!! A smart Data Scientist will always try minimizing the cost function, simultaneously acquiring the best possible result for the system, but when it comes to building the system it is always smarter to work on a cloud based platform for more features, better performance, ease of access, flexibility and most important FREE.

When you see FREE!!!!

NOTE : There are no unlimited free services.You will get credits for sign up.

REMINDER : Also if u are concern with the security of your data, the service providers are not obligated to the security of your contents till you are using them for free.

IV )Here are the 5 free platforms for notebook :

Google Colaboratory

  • It is a google product for research purpose,you just need to login to a google account.
  • Integration of html, google cloud, bash just to name a few.
  • gives you free usage of a K80 GPU.
  • It appears you get 12 hours of GPU time per instance and can do this multiple times per month.
  • Google Cloud Compute gives you 300 dollars worth of free credit after signing up, which is worth it.


  • It is a non-profit, open-source project by jupyter.
  • support interactive data science and scientific computing across all programming languages.
  • The best thing about this is and i quote “Jupyter will always be 100% open-source software” which i like the most.

Microsoft Azure

  • You get 10GB of storage, a maximum of 100 modules per experiment, and one hour execution time per experiment
  • Microsoft Azure gives you 200 dollars of free credits.
  • Featured libraries include python, R, F#, CNTK and other cloud services can be integrated into it.


  • cocalc have provided wonderful features like time travel, LaTeX editor, Markdown/HTML, history, Chat like features. There is not much in the free plan but they have some pretty good paid plans.
  • You will find all the necessary libraries for mathematics and Data Science purpose at your finger tip.
  • More people are enrolling for their “Teaching made easy” feature, find more here.


  • I have used it for ipynb but there are tons of things you can do here.
  • Basic environments such as LAMP, Ruby on Rails, JSP, Django, Node.js, Laravel

Although the title says free, these are some of the

I don’t want to create bias in your mind, and i assure all the above enlisted providers, provide best services. As more and more people are using cloud services they will become relatively cheap.

Cloud Services include :


Amazon Machine Learning is an incredibly popular service that guides users through creating ML models without needing to learn the complex algorithms themselves. Once you’ve created you models with the visualization tools and wizards, simple APIs create predictions for your application without any need for generating code or managing infrastructure.

The service figures out which fields are categorical and numerical and determines the accurate methods of data preprocessing on its own. However, Amazon ML does not allow any unsupervised learning methods, making it hard for beginners to develop their own understanding.

Features include:

  • GPU Instance: P3 instances provide up to 14 times better performance than previous-generation Amazon EC2 GPU compute instances. With up to 8 NVIDIA Tesla V100 GPUs, P3 instances provide up to one petaflop of mixed-precision, 125 teraflops of single-precision, and 62 teraflops of double-precision floating point performance.
  • Powerful compute: C5 instances are powered by 3.0 GHz Intel Xeon Scalable processors, and allow a single core to run up to 3.5 GHz using Intel Turbo Boost Technology. C5 instances offer higher memory to vCPU ratio and deliver 25% improvement in price/performance compared to C4 instances, and are ideal for demanding inference applications.

Google Cloud

  • So, it’s no surprise that they also have a cutting edge MLaaS platform available. Across their Cloud AI services, there’s a Machine Learning Engine, as well as services for natural language processing and APIs for speech, natural language processing, translation, video, and image recognition.
  • Data scientists should be excited to note that Google ML Engine is highly flexible and based off the ever-popular TensorFlow project. Of course, this platform is integrated with all the other Google services, but it’s mostly aimed at deep neural network tasks.

IBM Watson

  • Watson Machine Learning or WML is intended to address questions of deployment, operationalization, and even deriving business value from machine learning models. Users can keep utilizing their own Jupyter notebooks in Python, R, and Scala. Watson ML also boasts visual modelling tools that help users quickly identify patterns, gain insights, and make decisions faster.
  • You will need to create an account with Bluemix to start playing around with the service, but there’s a 30 day free trial and it’s pretty fun. After that, you need to choose between Lite, Standard, and Professional. While Lite is free as along as you stay under 5,000 predictions and 5 compute hours, Standard and Professional depend on how intensely you need your computing hours. Predictions run $0.40 — $0.50 per 1000 predictions.


  • MLaaS from Microsoft Azure is the ML Studio. It has something of a steep learning curve, since almost all operations must be completed manually, from data exploration, preprocessing, choosing methods, and validating modeling results. However, to make things easier, the browser-based environment is highly simplified with a visual drag-and-drop mechanism. No coding is necessary here!


  • When you click the above link you will be referred so u will get around 25$ of credits and then u may want to checkout the plans here.
  • There are various plans you can see for yourself here.


  • They have compelling features and have also given a comparison here.
  • It provides with all the basic features required


  • Above features are all the same
  • The free plan includes:

Total tasks & storage : unlimited

Data size : 16M or less

Max User : 1

If you want more on how to use the cloud services find it here.

Here are some more things you could refer: Alex Sanchez

In case you feel i have missed some points pls write to me at

why i made the page?

> My laptop doesn’t take much of the heavy stuff starting from Random forest, matrix generation, neural nets ,ensemble suff, gradient boosting,NLP . I personally couldn’t find free stuff where i could do the stuff and i wanted to beat the rich guys with heavy laptops. But how ??? that’s where i came around searched a bit and started using them. I felt other people like me would be facing similar problem.

I dedicated this article to the people like these.

sources : ||||| | |