Colaboratory for G Suite Reviews & Product Details

I have always used python code on my computer to build simple things, explore ideas or data, or automate tasks.

Colaboratory allows me to do this in the cloud, without needing to configure my computer - so I can do the same work on multiple devices, or share it with other people who do not have their devices set up. Review collected by and hosted on G2.com.

The interface is code only which can be quite messy. If we can build code but then have that in the background that would be good.

It also does not integrate into drive very easily, it's not always clear where files save.

File versioning is also messy, especially when sharing with people, as you are never sure what changes save and update the original or not.

Also it's very easy to destruct you work by mistake like accidentally deleting a cell you can not revert. Review collected by and hosted on G2.com.

it's very easy tool to be used to draft scripts, and make simulation of apps that can later be used on cloud. Moreover, allows with no setup to share pieces of codes with collegues and let them be autonomous on specific tasks Review collected by and hosted on G2.com.

it has limited UI capabilities, it's overwhelming for people with no code background to be used, unless the markdown formatting are used Review collected by and hosted on G2.com.

I really like the fact that it allows me to perform quick experiments with easy debugging and logging. Review collected by and hosted on G2.com.

Sometimes, the GPUs can turn out be insufficient to train large models. This hampers development quite a bit. Review collected by and hosted on G2.com.

An online python ready tool inside Google applications package. Review collected by and hosted on G2.com.

It loges out if not active and you need to run all the code again. Review collected by and hosted on G2.com.

Google Colab takes care of everything around the infrastructure requirements, software requirements and other associated things and lets us focus on solving real challenges in data science - we just need a browser to do even advanced machine learning training! The features l like most are the ability to share the notebooks with colleagues easily, the ability to use libraries like Tensorflow that automatically take care of version dependencies, the ability to use GPUs, and the ability to distributed training of neural network models that require TPUs, free of cost. Review collected by and hosted on G2.com.

There is nothing to dislike about Google Colab as it satisfies most of our standard requirements and has been very helpful for me in my data science work. To increase wider usage, Kaggle features like creating datasets, using notebooks as datasets etc., can be thought of Review collected by and hosted on G2.com.

Through Google Colaboratory, we can easily create, access and manage all our notebooks in a single place without worrying about where each notebook gets saved. It also has auto-save and versioning features, which lets us rest easy knowing that our latest changes are always saved, and we still have the option to fall back to an older version of code if needed. Another advantage is that we can run our notebooks on more capable hardware than our own system. Review collected by and hosted on G2.com.

A common disadvantage of using Colaboratory is the need to always be online to access our notebooks. There is also the fact that it can't offer the advantages of running Jupyter notebooks on our local machines such as one global installation of required libraries applies to all notebooks calling it. Review collected by and hosted on G2.com.

Simplicity to use by just a web browser, no need to install RAM hungry clients to access email, video call, chats and productivity tools. Review collected by and hosted on G2.com.

Less colorful graphics. It should come with more colorful themes and graphics. Review collected by and hosted on G2.com.

I like best about colaboratory for G Suite is that we can do data analysis seamlessly using Python and ML Models over the cloud interface. By this, we can also collaborate and contribute to the same project by our team members. It is easy to use, fast and efficient. Review collected by and hosted on G2.com.

Sometimes there is a lag in updates over the code. Libraries other than included with python needs to be installed again in every visit. Overall, a good experience. Review collected by and hosted on G2.com.

Colaboratory offers a cloud Python environment to write and execute code. When it comes to machine learning and data science/analytics, this comes in handy because of its GPU. Quick and fast command execution is made possible due to its cloud GPU. Also, no setup is required, unlike other products. Review collected by and hosted on G2.com.

I have been using Colaboratory for many years now and one thing which could be improved is its speed of loading new databases. Often while working offline, this becomes a hindrance. Review collected by and hosted on G2.com.

The best part about Colaboratory for G Suite is its accessibility; it is really amazing how easy it is to set up a notebook rather than create a whole new notebook (like Jupyter Notebook) using tools like Anaconda. If a user wants to work on a Python project, they can just open the Colaboratory, and one is good to go. Review collected by and hosted on G2.com.

Colaboratory for G Suite has a lot of things to improve; it becomes a little difficult to load the new database and data frames that are present offline. As the interpretation is done online, it gets a little slower as compared to its counterparts. Options are also limited. Review collected by and hosted on G2.com.