Colaboratory. A Google’s free tool for data scientists and developers

Those who create Machine Learning and advanced analytics models know very well that very important computing power is required to create and implement robust and efficient models. For the first stages of testing, it may be sufficient to work on your own machine, but as the size of the dataset grows, the power required for training the models can increase dramatically and not everyone can afford to purchase high-performance machines sized to the power of calculation required.

To help developers, data scientists, and data analysts in this activity there are various tools on the market, but none borders on simplicity combined with the power of Google Colaboratory, or rather Colab for friends. Google Colab is a free tool in the Google Suite that allows you to write python code directly from your browser. An online platform that offers a Jupyter notebook cloud hosting service where you can create rich documents that contain lines of code, graphics, text, links, and much more.

Therefore, it is a truly spectacular and useful tool for those involved in Data Science. And, for your pleasure, this is a really great introduction video to Google Colab.

Get started with Google Colaboratory (Coding TensorFlow)

Algorithms associated with complex data structures thanks to libraries and virtual machines

Colab is a free platform that allows anyone to write and run Python code through a browser and the only requirement is to have a Google account like Gmail. Colaboratory, as we said before, is based on an Open Source project called Jupyter, and the documents and programs written on Colab are called Notebooks, also they will be saved automatically on the Google Drive associated with your account. Colab’s Notebooks can be shared with other users who can execute code written by you or also modify it.

The Jupiter notebook will then run on Google server virtual machines; this allows you to free yourself from the hardware part and concentrate solely on the Python code and on the contents that you want to integrate into the notebook.

The virtual machines made available in Google Colab host a configured environment that allows you to immediately focus on Data Science projects: there are numerous Python libraries, including many of Data Science such as Keras and Tensorflow, you can take advantage of GPU and TPU to give important computational boosts to our work, for example in the implementation of neural networks with Tensorflow.

Importing data into Google Colab is very simple: by manually loading the data, or by using the connectors made available to access our datasets present for example in our Google Drive.

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