Saturn Cloud
Amazon SageMaker
Databricks Unified Analytics Platform
Apache Zeppelin
Azure Synapse Analytics
Deepnote
Google BigQuery
GeoSpock
GitHub Pages
Vercel
Jekyll
Netlify
Cloudflare Pages
surge.sh
Neocities
GitHub
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 GitHub Pagesfast, 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, GitHub Pages seems to be a lot more popular than Saturn Cloud. While we know about 504 links to GitHub Pages, 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 / over 3 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: over 3 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
The site itself is a statically generated Next.js app, built in CI and deployed to GitHub Pages via actions/deploy-pages. No server to manage, no hosting bill. - Source: dev.to / 3 months ago
Static sites are fast and cheap to host, but your data goes stale the moment you deploy. This post shows how a SvelteKit portfolio site serves live data from five external sources while still deploying as static HTML to GitHub Pages. - Source: dev.to / 3 months ago
All three themes are designed for accessible deployment. You can host them for free on Netlify, GitHub Pages, Vercel, or Cloudflare Pages. The only cost is a domain name (which can be as cheap as $5/year on Porkbun). - Source: dev.to / 5 months ago
This action can store collected benchmark results in GitHub pages branch and provide a chart view. Benchmark results are visualized on the GitHub pages of your project. - Source: dev.to / 9 months ago
But that's not the case. The blog is a simple static generated website using Jekyll, it is built and served through GitHub Pages. With that in mind it makes more sense to use tools and leverage tool calling. - Source: dev.to / 10 months ago
Amazon SageMaker - Amazon SageMaker provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly.
Vercel - Vercel is the platform for frontend developers, providing the speed and reliability innovators need to create at the moment of inspiration.
Databricks Unified Analytics Platform - One platform for accelerating data-driven innovation across data engineering, data science & business analytics
Jekyll - Jekyll is a simple, blog aware, static site generator.
Apache Zeppelin - A web-based notebook that enables interactive data analytics.
Netlify - Build, deploy and host your static site or app with a drag and drop interface and automatic delpoys from GitHub or Bitbucket
