Saturn Cloud
Amazon SageMaker
Databricks Unified Analytics Platform
Apache Zeppelin
Azure Synapse Analytics
Deepnote
Google BigQuery
GeoSpock
Gitpod
GitHub Codespaces
replit
Codeanywhere
AWS Cloud9
CodeSandbox
Coder
Koding
Saturn Cloud is an award-winning ML platform with 75,000+ users, including NVIDIA, CFA Institute, Snowflake, Flatiron School, Nestle, and more. It is an all-in-one solution for data science & ML development, deployment, and data pipelines in the cloud. Users can spin up a notebook with 4TB of RAM, add a GPU, connect to a distributed cluster of workers, build large language models, and more in a completely hosted environment.
Data scientists and analysts work best using the tools they want to use. You can use your preferred languages, IDEs, and machine-learning libraries in Saturn Cloud. We offer full Git integration, shared custom images, and secure credential storage, making scaling and building your team in the cloud easy. We support the entire machine learning lifecycle from experimentation to production with features like jobs and deployments. These features and built-in tools are easily shareable within teams, so time is saved and work is reproducible.
Saturn Cloud Gitpodfast, easy to create container, clear bill
I have used many alternative platform but nothing comes close to this
Smooth and bug free experience. There are ready data science images with pre loaded packages for most common scenarios, making you focus on the project/problem and leave the infrastructure part to Saturn Cloud.
Based on our record, Gitpod seems to be a lot more popular than Saturn Cloud. While we know about 76 links to Gitpod, we've tracked only 7 mentions of Saturn Cloud. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
After the MLOps tooling evaluation, our focus shifted to data engineering. Some teams in the company were already using tools like Dask and xarray to manage and process their datasets. The architect was determined to build a data lake for the organization. The vision was to make xarray datasets accessible via Intake, using a Dask-capable computing platform. For the compute platform, we explored services like... - Source: dev.to / over 1 year ago
Not 100% sure of your intention, but if you work with python, and you're familiar with (or can spend the time learning) dask, and willing to pay, you can consider coiled.io or saturncloud.io that offer managed dask that you can scale and use GPUs etc (again, not sure if applicable to your use case). Source: over 3 years ago
SaturnCloud - Data science cloud environment, that allows to run Jupyter notebooks and Dask clusters. 30 hours free computation and 3 hours of Dask per month. - Source: dev.to / almost 4 years ago
I think your site looks good and I have used the type of service you offer, but there are 2 potential problems. As SheepherderPatient51 said,Google already offers all of this for free (and so does https://kaggle.com and https://www.paperspace.com ). There are also other sites just like yours such as https://deepnote.com,https://saturncloud.io, and https://lambdalabs.com . Source: almost 4 years ago
* How does it differ from other GPU cloud providers that offer ready to use Jupyter notebooks? (E.g. https://support.genesiscloud.com/support/solutions/articles/47001170102-running-jupyter-notebook-or-jupyterlab-on-your-instance or https://saturncloud.io/). - Source: Hacker News / over 4 years ago
# Example of setting up a Gitpod workspace # Open your repository in Gitpod with one click Https://gitpod.io/#https://github.com/your-repo. - Source: dev.to / over 1 year ago
For my part, I often develop on cloud environments. I was lucky to come across Gitpod in 2019 and I have been using it everyday since, whether for Zenika projects, personal projects or open source projects. - Source: dev.to / about 2 years ago
We will use VScode workspace running on Gitpod as an IDE, you can use VScode on your local machine but you need to skip steps or change some details related to Gitpod. We will begin by setting up the workspace, preparing the requirements, and installing the dependencies. - Source: dev.to / almost 2 years ago
Next, we need to install Docker by downloading it from the official website if you haven't already. Alternatively, use a free online platform like Gitpod or a VPS to run a Docker instance, if possible. Otherwise, install it on your local computer. - Source: dev.to / about 2 years ago
If you prefer instead to have a look at a fully working & effect-native app we've prepared a demo cli app that you can directly open in Gitpod or locally (if you prefer), you'll need to provide an OpenAI API Key in order to integrate with the OpenAI API. The demo app allows you to train a model via embeddings from a set of files and then allows you to prompt the trained model with questions. - Source: dev.to / over 2 years ago
Amazon SageMaker - Amazon SageMaker provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly.
GitHub Codespaces - GItHub Codespaces is a hosted remote coding environment by GitHub based on Visual Studio Codespaces integrated directly for GitHub.
Databricks Unified Analytics Platform - One platform for accelerating data-driven innovation across data engineering, data science & business analytics
replit - Code, create, andlearn together. Use our free, collaborative, in-browser IDE to code in 50+ languages โ without spending a second on setup.
Apache Zeppelin - A web-based notebook that enables interactive data analytics.
Codeanywhere - Codeanywhere is a complete toolset for web development. Enabling you to edit, collaborate and run your projects from any device.