Amazon S3 (Amazon Simple Storage Service) is the storage platform by Amazon Web Services (AWS) that provides an object storage with high availability, low latency and high durability. S3 can store any type of object and can serve as storage for internet applications, backups, disaster recovery, data archives, big data sets and multimedia.
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.
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.
True story, way better than just sweating Colab. The best and cheapest compute services there is.
Based on our record, Amazon S3 seems to be a lot more popular than Saturn Cloud. While we know about 198 links to Amazon S3, 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.
Takeaway: S3 is feature-rich and great for complex workflows. Cloud Storage is simpler and faster for global access. Explore S3 documentation. - Source: dev.to / 7 days ago
To address this, the team introduced a conditional frontend build mechanism. Using git diff with the three-dot notation, it detects whether a PR includes frontend changes compared to the main branch. If no changes are detected, the frontend build step is skipped, reusing a prebuilt version stored in AWS S3 and served via an internal Content Delivery Network (CDN). - Source: dev.to / 29 days ago
In this article, we present an architecture that demonstrates how to collect application logs from Amazon Elastic Kubernetes Service (Amazon EKS) via Vector, store them in Amazon Simple Storage Service (Amazon S3) for long-term retention, and finally query these logs using AWS Glue and Amazon Athena. - Source: dev.to / about 1 month ago
Iceberg has quietly become the foundation of the modern data lakehouse. More and more engineering teams are adopting it to store and manage analytical data in cloud storage — like Amazon S3, Google Cloud Storage, or Azure Data Lake Storage — while freeing themselves from the limitations of closed systems. - Source: dev.to / about 2 months ago
AWS Lambda is perfect for applications that process images due to its integration with AWS S3, an object storage service. A good example is an e-commerce application that renders images in different sizes. Here are the top features:. - Source: dev.to / 2 months ago
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 / 5 months 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 2 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 2 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 2 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 / about 3 years ago
Google Cloud Storage - Google Cloud Storage offers developers and IT organizations durable and highly available object storage.
Amazon SageMaker - Amazon SageMaker provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly.
Wasabi Cloud Object Storage - Storage made simple. Faster than Amazon's S3. Less expensive than Glacier.
Databricks Unified Analytics Platform - One platform for accelerating data-driven innovation across data engineering, data science & business analytics
AWS Lambda - Automatic, event-driven compute service
Deepnote - A collaboration platform for data scientists