What is Google Colab and Why You Should Use It in 2025?
- Manali Sharma
- Feb 26
- 4 min read
Updated: Feb 28
What is a Colab? It's Google's powerful cloud computing tool that gives us free access to GPUs, TPUs, and essential Python libraries without installing anything on our computers.
We've found that Google Colab works seamlessly through your browser, specifically built on Project Jupyter. Because it allows us to store our notebooks directly in Google Drive and comes with pre-installed Python modules, it is perfect for both inexperienced and seasoned users.
Official Link: https://colab.research.google.com
What Can I Do in Google Colab?
Google Colab is a feature-rich and highly customizable platform designed for Python programming, data science, and machine learning. It is ideal for beginners and experts alike since it provides cloud-based execution, eliminating the need for local installs.
The tasks you can do in Google Colab are detailed below, along with their real-world applications.
Why Google Colab Remains Relevant in 2025?
Google has been making Colab work better with its other tools, like Google Cloud and BigQuery. This means you can easily connect your Colab notebooks to those services. It's pretty handy for managing big projects and handling complicated tasks. No more switching between different platforms. You can import and work with large datasets right in Colab. It's simple and efficient.
Even though other platforms are improving, Google Colab still stands out. It’s a top choice for data scientists and AI developers. One of the biggest reasons is that you can run code on GPUs for free! That's huge for startups, researchers, and students who need powerful computing without the cost.
Plus, with the better integration of Google Cloud, BigQuery, and Vertex AI, Colab becomes even more useful for big projects. It gives you the tools you need to work on extensive datasets and apply complex algorithms without any hassle.
Google Colab: A Complete Guide to Its Features & Benefits
One of the best things about Google Colab is that it has native support for popular machine learning libraries like TensorFlow, PyTorch and Scikit-learn. Since it’s in the cloud, you don’t have to manually install packages or configure drivers to use hardware acceleration. And it has integrated storage with Google Drive so you can save and share your notebooks easily.
Google Colab truly shines when it comes to machine learning projects. The platform's pre-configured environment makes training basic models straightforward and efficient. For instance, you can start with:
Image Recognition: Train models to recognize handwritten digits using the MNIST dataset
Basic Deep Learning: Create simple neural networks using Keras API
Data Classification: Build models for categorizing various types of data
The platform offers flexibility in handling datasets from multiple sources. For Kaggle datasets, you'll need to:
Generate an API token from your Kaggle account
Upload the token to Colab
Use the Kaggle API to download datasets directly
One significant advantage of using Colab for ML projects lies in its GPU/TPU access. These accelerated computing options substantially speed up model training compared to standard CPU processing. To optimize your workflow:
Choose GPU/TPU runtime for smaller models
Configure runtime settings based on project requirements
Save trained models for future use
Utilize TensorBoard for visualizing training progress
For data storage during projects, Colab provides integration with both Google Drive and Google Cloud Platform. This enables seamless access to large datasets and secure storage of trained models. Additionally, the platform supports real-time collaboration, making it ideal for team projects and educational settings.
In general, Google Colab is a great alternative to a local Jupyter Notebook with free GPU/TPU acceleration, effortless Google Drive integration, and live collaboration. Unlike Jupyter, which involves manual configuration and local computing resources, Colab eliminates these constraints, making it the perfect solution for machine learning tasks.
How do I get started with Google Colab?
Getting started with Google Colab is super easy. Just go to official link. You’ll need to log in using your Google account. Once you’re in, you can do a bunch of things!
You can create a new notebook right away. If you have files saved in Google Drive, you can upload them too. Plus, if you’ve got notebooks saved on GitHub, you can bring those into Colab as well.
Once everything is set up, you can start coding. It’s really simple! You can also train models and look at your data in a clear way. In just a few minutes, you’ll be up and running.
What Happens After 12 Hours of Google Colab?
Google Colab has a session timeout policy to efficiently manage resources and ensure fair access for everyone. Colab will automatically shut down a running notebook after 12 hours (or sooner if it is idle).
This means that unless precautions such as reloading data when restarting or saving progress to Google Drive are taken, all saved variables, runtime states, and imported datasets would be lost.
Google Colab offers three levels, with varying runtime limits and hardware availability:
User Type | Session Duration | Hardware Access | Memory Limit |
|---|---|---|---|
Free Users | Up to 12 hours | Limited GPU/TPU | Standard Memory |
Colab Pro | Up to 24 hours | Faster GPUs (Tesla T4) | Higher Memory |
Colab Pro+ | Even longer sessions | Powerful GPUs (A100, V100) | Maximum Memory |
Google Colab Session Lifecycle
When Google Colab session expires, users need to restart their kernel and reload data in order to work further. The following flowchart illustrates this process:
Is Google Colab Good for Coding?
Google Colab is one of the best platforms for coding, especially for Python-based projects. Here’s why:
Best for Python Development
Pre-installed libraries such as NumPy, Pandas, TensorFlow, and Matplotlib included, no need for manual installation.
Easy to code, run, and debug Python programs with a native Jupyter Notebook interface.
Great for Machine Learning & Data Science
GPU/TPU acceleration drastically accelerates deep learning model training.
Integrated Google Drive, GitHub, and BigQuery direct access makes data set management a breeze.
Suitable for Beginners & Professors
Interactive coding environment, so ideal for Python learning & AI courses.
No complicated local setup, just open a notebook and code immediately.
Google Colab is still a robust, free, and open platform for coding, machine learning, and data science. You can be a beginner looking to learn Python or an AI scientist training deep learning models. Colab provides a seamless experience with cloud resources. So, what are you waiting for? Give it a try!
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