Saturn Cloud
Amazon SageMaker
Databricks Unified Analytics Platform
Apache Zeppelin
Azure Synapse Analytics
Deepnote
Google BigQuery
GeoSpock
Apache HTTP Server
Microsoft IIS
Apache Tomcat
LiteSpeed Web Server
Forge
Phusion Passenger
Eclipse Jetty
XAMPP
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.
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fast, 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, Apache HTTP Server seems to be a lot more popular than Saturn Cloud. While we know about 71 links to Apache HTTP Server, 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
Choose between Apache or Nginx as your web server and configure it to handle API requests. For Apache, use an .htaccess file in your project directory to enable clean URLs and route all traffic through your main PHP file. Nginx requires similar adjustments in its server block configuration. - Source: dev.to / 11 months ago
Hetzner, Docker, Traefik as proxy and Apache / httpd inside the containers. - Source: dev.to / about 1 year ago
The license was notably used in web server software, such as in projects that led to the creation of the Apache HTTP Server. Its source disclosure requirement helped build trust among early developers, and community improvements drove rapid innovation during the early stages of the internet. - Source: dev.to / about 1 year ago
Enterprise Software Frameworks: Companies developing robust, scalable solutions now favor licenses that not only protect against unauthorized commercial exploitation but also encourage contributions. Just as the Apache HTTP Server has been a success story for open source, frameworks built on the HSQLDB License have gained traction for their fairness. - Source: dev.to / about 1 year ago
Web Servers and Infrastructure Projects: Projects like the widely used Apache HTTP Server emphasize stability and developer fairness. SISSL 1.1 has been utilized in similar infrastructure projects to provide legal certainty while preventing exploitation of contributions. - Source: dev.to / about 1 year ago
Amazon SageMaker - Amazon SageMaker provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly.
Microsoft IIS - Internet Information Services is a web server for Microsoft Windows
Databricks Unified Analytics Platform - One platform for accelerating data-driven innovation across data engineering, data science & business analytics
Apache Tomcat - An open source software implementation of the Java Servlet and JavaServer Pages technologies
Apache Zeppelin - A web-based notebook that enables interactive data analytics.
LiteSpeed Web Server - LiteSpeed Web Server (LSWS) is a high-performance Apache drop-in replacement.