Google Colab came out as a boon for machine learning practitioners — not only to solve the storage problems of working with a large dataset but also financial constraints of affording a system that meets data science work requirements. The Jupyter notebook environment running on the cloud with no requirement for a separate setup was designed to equip ML enthusiasts to learn, run, and share their coding with just a click. Its free access to python libraries, 50 GB hard drive space, 12 GB RAM, and a free GPU makes it a perfect bet for ML practitioners.
Despite all these advantages, in reality, Google Colab comes with several disadvantages and limitations, restricting a machine learning practitioners’ coding capability to run without any speed bumps. Let’s look at these features of Google Colab that can spoil machine learning experiences.
Also Read: The Beginner’s Guide To Using Google Colab
Subscribe to our Newsletter
Join our editors every weekday evening as they steer you through the most significant news of the day, introduce you to fresh perspectives, and provide unexpected moments of joy
Drawbacks Of Google Colab
Closed-Environment: Anyone can use Google Colab to write and run arbitrary Python code in the browser. However, it is still a relatively closed environment, as machine learning practitioners can only run the python package already pre-added on the Colab. There is no way that one can add their own python package and start running the code. Hence, the platform can provide common tools but is not suitable for specialisation.
Repetitive Tasks: Imagine one has to repeat the same set of actions repeatedly to execute a task — not only will it be exhausting, but it will also consume a lot of time. Similarly, for every new session in the Google Colab, a programmer must install all of the specific libraries that aren’t included with the standard Python package.
No Live-Editing: Writing a code and sharing the same with your partner or a team allows you to collaborate. However, the option for live editing is completely missing in Google Colab, which restricts two people to write, or edit codes at the same time. Hence, it further leads to a lot of back and forth re-sharing. Additionally, this feature is provided by its other competitors, including CoCalc.
Saving & Storage Problems: Uploaded files are removed when the session is restarted because Google Colab does not provide a persistent storage facility. So, if the device is turned off, the data can get lost, which can be a nightmare for many. Moreover, as one uses the current session in Google Storage, a downloaded file that is required to be used later needs to be saved before the session’s expiration. In addition to that, one must always be logged in to their Google account, considering all Colaboratory notebooks are stored in Google Drive.
Limited Space & Time: The Google Colab platform stores files in Google Drive with a free space of 15GB; however, working on bigger datasets requires more space, making it difficult to execute. This, in turn, can hold most of the complex functions to execute.
Google Colab allows users to run their notebooks for at most 12 hours a day, but in order to work for a longer period of time, users need to access the paid version, i.e. Colab Pro, which allows programmers to stay connected for 24 hours. Finally, the less talked about drawback of the platform is its inability to execute codes or run properly on a mobile device.
Google Colab entered the market with a pure focus to provide machine learning practitioners with a platform and tools to advance their machine learning capabilities. However, over time, the volume, intensity, and quality of data changed, and so did ML practitioners’ requirements to find solutions to complex problems. Coming out with a paid version is easy, but for the larger good, it needs to be upgraded and freely accessible to anyone for the entire machine learning ecosystem to grow.