Google Colaboratory is a free online cloud-based Jupyter notebook environment that allows us to train our machine learning and deep learning models on CPUs, GPUs, and TPUs.
Now a days Machine Learning is playing a key role in our life. As a programmer we should familiar with Artificial Intelligence but sometimes our computer might not support enough requirements to learn Machine Learning or lower processing power is make trouble to learn.
What is Google Colab?
Google Colab is a platform like coding environment that is well-suited for machine learning and data analysis.
In Colob there are Machine Learning libraries and this offers GPU usage. It is mainly used by data scientists and ML engineers.
Yes, Google Colab is free to use and you can access all of its features to a certain degree. There is a subscription plan called Google Colab Pro that offers more processing power, RAM, and memory.
Pros
- Google Colab is Free
- Easy to get started
- Allows access to GPUs/TPUs
- Easy to share code with others
- Easy graphical visualizations in Notebooks
Cons
- GPU/TPU usage is limited
- Not the most powerful GPU/TPU setups available
- Not the best de-bugging environment
- It is hard to work with big data
- Have to re-install extra dependencies every new runtime
All of these features make Google Colab a phenomenal asset when it comes to collaborative, data science, machine learning, and data analysis projects.
Alternatives to Google Colab
- Jupyter Notebook
- Kaggle
- Azure Notebooks
- Amazon SageMaker
- Paperspace Gradient
- FloydHub
In my point of view
As a Machine Learning enthusiast and learner I really like to use Google Colob and in some cases if we are dealing with more confidential projects on machine learning that has a unique idea you can do it with locally. In other case if you want more efficiency in colob you can buy pro.
