Deepnote stands out as the most full-featured option, with its fast environment loading, AI assistance, live collaboration, and publishing capabilities. The top options provide great value with free GPUs, easy setup, collaboration features, and integrations with other services. In conclusion, cloud notebooks have become essential tools for data scientists and analysts to efficiently carry out their work. ![]() The only good part is that it provides free storage and computing. It has some features that you can get in Deepnote, but the UI is confusing for any beginner to get used to it. ![]() It seems like JetBrains have forgotten about it. I used to run my code on Datalore, but since its launch, there haven't been much improvement or changes to the platform. JetBrains Datalore is similar to Noteable, but it is slow and lacks some key features. Apart from that, it is the JupyterLab on the cloud. It does provide free credit every month for you to run and execute code. This platform provides a low-code solution for creating powerful data products by combining automation, analytics, and AI. Naas is known for its data templates for all kinds of problems. It is not a complete data science platform that you want to use everyday. Things have changed over the years as they limit the free tier and focus on paid options.Īpart from easy access to free GPU and fast loading time, there is little to Google Colab. We use them to run our deep learning code, and sometimes it is an excellent and convenient tool. Google Colab is the same old cloud notebook that we love and cherish. This feature makes it highly valuable in the notebook category. Additionally, you can connect it with ChatGPT to generate and run code with outputs. The best part about this platform is its minimalist design. This platform provides data connection, loading, versioning, publishing of notebooks, live collaboration, and fast environment loading. It is simple, fast, and comes with all kinds of features. I came across Noteable when it was introduced as a ChatGPT plugin. Once the data is connected, users can analyze it using either SQL or Python directly within interactive notebooks. It allows users to connect to a variety of data sources, including databases, cloud storage, and APIs. Hex is a modern Data Workspace that aims to make working with data easier and more collaborative. It provides a similar feature to Deepnote, but due to slow environment loading and code running. Hex is now available to the public, and it is the popular option for your data science and analytics tasks. It is my go-to platform when I am participating in a competition or experimenting with deep learning models.Īgain, I highly recommend Kaggle due to its strong community and high-tier hardware for you AI projects. Moreover, you get free storage, access to open-source datasets and code, Google Cloud integrations, and versioning. With Kaggle, you get high-tier CPUs, GPUs, and TPUs for free. The only thing they need to catch up on is live collaboration and commenting. For example, they are adding new high-tier GPUs, scheduling runs, dedicated tabs for models, and quickly loading the dataset. With Deepnote, Kaggle has also introduced new features this year. It has become easier even for non-technical professionals to write and debug the code using the Deepnote AI feature. I will highly recommend you create an account and experience it yourself. It also supports all kinds of programming languages, and you can create your own environment using Docker Hub. ![]() You can start a machine in less than a minute and benefit from a pre-built development environment.
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