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Cloud laptops have become the norm today for data scientists and analysts to run their code and generate analytical reports. Cloud notebooks provide a browser-based interface to write and run code without the need to install anything locally. Additionally, high-end hardware can be accessed to accelerate machine learning research and development.
Cloud laptop platforms offer more than just free computing and pre-built environments. They also offer third-party tool integrations, collaborations, and publishing options. In this blog, we will explore the seven best cloud laptops and their best features. Using these features can enhance your current data science development stack.
deep note It’s on top now. Because? Recently, they have introduced new features that will simplify your development experience. I love the platform, the team and the community. Additionally, I use it for all data science and machine learning projects.
You can start a machine in less than a minute and benefit from a pre-built development environment. It also supports all types of programming languages and you can create your own environment using Docker Hub.
I highly recommend you create an account and experience it yourself. It has become easier even for non-technical professionals to write and debug code using the Deepnote ai feature.
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With Deepnote, Kaggle It has also introduced new features this year. For example, they are adding new high-end GPUs, scheduling runs, dedicated tabs for models, and fast loading the dataset. All they need to catch up is live collaboration and feedback.
With Kaggle, you get high-end CPUs, GPUs, and TPUs for free. Plus, you get free storage, access to open source code and datasets, integrations, and Google Cloud version control.
It is my go-to platform when I participate in a competition or experiment with deep learning models.
Again, I recommend Kaggle because of its strong community and high-end hardware for your ai projects.
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tech/” rel=”noopener” target=”_blank”>Hex It is now available to the public and is the popular choice for your data science and analytics tasks. It provides a similar feature to Deepnote, but due to slow environment loading and code execution. I have ranked it in third place. It is also limited in many ways.
Hex is a modern data workspace that aims to make working with data easier and more collaborative. It allows users to connect to a variety of data sources, including databases, cloud storage, and APIs. Once data is connected, users can analyze it using SQL or Python directly within interactive notebooks.
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I came across remarkable when it was introduced as ChatGPT plugin. Before that, I had no idea it existed. It’s simple, fast, and comes with all kinds of features.
This platform provides data connection, uploading, version control, notebook publishing, live collaboration, and fast environment loading. The best part of this platform is its minimalist design. Additionally, you can connect it with ChatGPT to generate and execute code with outputs. This feature makes it very valuable in the laptop category.
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Google Co. It’s the same old cloud notebook that we love and appreciate. We use them to run our deep learning code and sometimes it is a great and convenient tool. Things have changed over the years as they limit the free tier and focus on paid options.
Aside from easy access to free GPU and a fast loading time, Google Colab has little going for it. It’s not a complete data science platform that you want to use every day.
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ai/” rel=”noopener” target=”_blank”>naas is known for its data templates for all types of problems. This platform provides a low-code solution to create powerful data products by combining automation, analytics and artificial intelligence.
It has limited computing and functions. Provides free credit every month so you can run code. Other than that, it’s JupyterLab in the cloud.
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JetBrains Data Insights It is similar to Noteable, but is slow and lacks some key features. You are also limited by computing. I used to run my code on Datalore, but since its release, there haven’t been many improvements or changes to the platform. It seems that JetBrains has forgotten about it.
It has some features that you can get in Deepnote, but the user interface is confusing for any beginner to get used to. The only good thing is that it provides free storage and computing.
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In conclusion, cloud laptops have become essential tools for data scientists and analysts to do their work efficiently. The best options offer great value with free GPUs, easy setup, collaboration features, and integrations with other services. Deepnote stands out as the most feature-rich option, with its fast environment loading, ai assistance, live collaboration, and publishing capabilities.
Kaggle is great for working with deep learning given its access to high-end hardware. Hex and Noteable offer modern interfaces and integrations like ChatGPT. While Google Colab and others have their specific uses, Deepnote seems to lead the pack with its focus on end-to-end data science workflow. Whichever platform you choose, cloud notebooks will undoubtedly improve your data science projects and your ability to gain insights.
Abid Ali Awan (@1abidaliawan) is a certified professional data scientist who loves building machine learning models. Currently, he focuses on content creation and writing technical blogs on data science and machine learning technologies. Abid has a Master’s degree in technology Management and a Bachelor’s degree in Telecommunications Engineering. His vision is to build an artificial intelligence product using a graph neural network for students struggling with mental illness.
