Saturn Cloud provides a hosted environment where it's possible to work with various software programming tools (e.g., Jupyter Python notebooks, Julia, R and more). The system is containerized and accessible both via Jupyter Notebook web pages and SSH—a feature that Google Colab restricts to PRO subscriptions only. I’m currently working on porting a machine learning project to CPU, which provides image Segmentation via Large Language Models. This project handles both image description, image analysis and image object segmentation. Since this project currently relies on CUDA and my local PC has no Nvidia GPUs, I’ve found the computational resources and ease of use provided by Saturn Cloud to be invaluable.
Works at a tech consulting company with 51-200 employees
Real User
Top 10
Mar 1, 2024
I'm leveraging a cloud-based platform for competitive machine learning. Tight deadlines and resource-intensive models demand powerful hardware. The cloud provides scalable GPUs and RAM, letting me experiment with cutting-edge architectures without limitations. Its collaborative features are perfect for distributed teams, enabling seamless code sharing and analysis. I stay focused on model development, not infrastructure, thanks to the platform's streamlined setup. My toolkit – Python, Jupyter Notebooks, and standard data science libraries – works seamlessly in the cloud environment. This ensures a smooth transition from local prototyping to large-scale competition training.
We use the solution for development, testing, and experiments. Here are some examples of how we use it: 1) There are some workloads that would be really uncomfortable to test in Visual Studio and require more than Google Collab and Azure Notebooks. 2) Testing that's pretty extensive and that's great to have a sensible offering ready in just a few minutes. 3) Saturn's R environment offering is by far the best cloud offering. Lots of time was saved on building a stable environment just by working with Saturn's offering. 4) Some experiments involving code that would crash other environments were performed in a stable, isolated, and secure way.
We use Saturn Cloud to perform data analysis on large volumes of data. Saturn fetches and updates data, We can use it for machine learning training and prediction, and perform experimental work on various data using multiple machine learning techniques. In some cases, parallel computation is also required to perform the analysis as quickly as possible. The environment has eight CPU cores and 64 GB RAM. In some cases, we are using GPU. The development environment includes Python, Scikit Learn, XGBoost, Jupyter Lab, and Jupyter Notebook.
I use Saturn Cloud to run my machine learning models. Since it is super fast and has lots of choices of language support and libraries. It also gives storage for my large datasets (images, text corpus..). For example, one project was OCR (computer vision). It can scan an answer sheet and grade it automatically. When I need to fine-tune and tweak the models, it was impressive. Saturn Cloud makes it so easy and fast to do so. This is an advantage in the industry when you can do prototype and software iteration quickly.
Saturn Cloud is a cloud-based data science and machine learning platform that provides a scalable, flexible, and easy-to-use environment for data scientists and machine learning engineers. Saturn Cloud offers a variety of features and tools for data science, including: Compute resources (including CPUs, GPUs, and Dask clusters), Storage (object, block, and ephemeral storage), Networking, a variety of integrations with ML tools such as JupyterLab, RStudio, and TensorFlow.
Saturn Cloud is a...
Saturn Cloud provides a hosted environment where it's possible to work with various software programming tools (e.g., Jupyter Python notebooks, Julia, R and more). The system is containerized and accessible both via Jupyter Notebook web pages and SSH—a feature that Google Colab restricts to PRO subscriptions only. I’m currently working on porting a machine learning project to CPU, which provides image Segmentation via Large Language Models. This project handles both image description, image analysis and image object segmentation. Since this project currently relies on CUDA and my local PC has no Nvidia GPUs, I’ve found the computational resources and ease of use provided by Saturn Cloud to be invaluable.
I'm leveraging a cloud-based platform for competitive machine learning. Tight deadlines and resource-intensive models demand powerful hardware. The cloud provides scalable GPUs and RAM, letting me experiment with cutting-edge architectures without limitations. Its collaborative features are perfect for distributed teams, enabling seamless code sharing and analysis. I stay focused on model development, not infrastructure, thanks to the platform's streamlined setup. My toolkit – Python, Jupyter Notebooks, and standard data science libraries – works seamlessly in the cloud environment. This ensures a smooth transition from local prototyping to large-scale competition training.
We use the solution for development, testing, and experiments. Here are some examples of how we use it: 1) There are some workloads that would be really uncomfortable to test in Visual Studio and require more than Google Collab and Azure Notebooks. 2) Testing that's pretty extensive and that's great to have a sensible offering ready in just a few minutes. 3) Saturn's R environment offering is by far the best cloud offering. Lots of time was saved on building a stable environment just by working with Saturn's offering. 4) Some experiments involving code that would crash other environments were performed in a stable, isolated, and secure way.
We use Saturn Cloud to perform data analysis on large volumes of data. Saturn fetches and updates data, We can use it for machine learning training and prediction, and perform experimental work on various data using multiple machine learning techniques. In some cases, parallel computation is also required to perform the analysis as quickly as possible. The environment has eight CPU cores and 64 GB RAM. In some cases, we are using GPU. The development environment includes Python, Scikit Learn, XGBoost, Jupyter Lab, and Jupyter Notebook.
I use Saturn Cloud to run my machine learning models. Since it is super fast and has lots of choices of language support and libraries. It also gives storage for my large datasets (images, text corpus..). For example, one project was OCR (computer vision). It can scan an answer sheet and grade it automatically. When I need to fine-tune and tweak the models, it was impressive. Saturn Cloud makes it so easy and fast to do so. This is an advantage in the industry when you can do prototype and software iteration quickly.