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One of the great things about Gradient is that it provides valuable functionality for beginners to professionals, with an intuitive web UI and extremely low barrier to entry. It’s comprised of a web UI, CLI, and SDK. Gradient simplifies developing, training, and deploying deep learning models.

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Gradient is an end-to-end MLOps platform that includes a free hosted Jupyter notebook service, with many options for pre-configured environments and free GPUs and CPUs. Many other hosted Jupyter environments will outperform Google Colab on one or all of these points. When choosing a hosted Jupyter notebook service, you might take into account features like:

COLLABORATORY GOOGLE DEPENDENCIES FULL

For instance, other cloud-hosted notebook providers will support version control and easy integration with a full MLOps pipeline. Colab features are essentially limited to Python support and the ability to share notebooks on Google Drive, which are both quite standard. Unfortunately, dealing with big datasets is a pretty standard part of most ML pipelines, thus making Colab's slow storage reason enough for many users to search for an alternative Jupyter host.Īlthough Colab might meet the needs of some hobbyists, in contrast to other providers, Colab doesn’t provide many additional features for a comprehensive data science/ML workflow. Users report Colab repeatedly timing out if they have too many files in a directory, or failing to read files with obscure and nondescript errors.

When it needs to ingest large quantities of data, Colab will start to crawl. Other providers, on the other hand, will guarantee the entire session and allow you to pick up where you left off, even if you're not connected the entire time.Īnother disadvantage to Colab is its extremely slow storage. Imagine waiting hours for your model to train, just to come back and see that your instance was shut down or imagine having to keep your laptop open for 12 hours, afraid that it will go into sleep mode and disconnect you. This means that you can lose your work and any training progress – also if you happen to close your tab, or log out by accident. Perhaps the biggest complaint of Colab users is that instances can be shut down (“preempted”) in the middle of a session, and disconnect if you're not actively connected to your notebook. Just a few of the drawbacks to Google Colab include:

That then begs the question: Why Shouldn’t I Use Google Colab?ĭespite being a popular choice, Colab faces several issues that are deal breakers for many users. With free GPUs and storage linked to Google Drive, many users in the ML and data science communities find it a natural extension of their Google-centric web existence. In recent years, Google Colab has become a popular choice for cloud-backed notebooks. By taking care of all of the hardware and backend configuration, cloud-hosted environments also enable users to focus on their work, without any messy installation, configuration, or hardware purchases. In addition to powerful compute resources that might be difficult to get locally (or which would break the bank if you tried), cloud-hosted Jupyter environments come with features like cloud storage, model training and deployment capabilities, version control, and more. There's now a huge selection of options to choose from when it comes to cloud-hosted notebook services, so we decided to put together a list of the best available options today. Jupyter notebooks have become the go-to standard for exploring machine learning libraries and algorithms.

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