Works at a tech consulting company with 51-200 employees
Real User
Top 20
Mar 1, 2024
My main suggestion for improvement centers on pricing. Introducing a tier modelled after AWS spot instances would be a game-changer. Users could bid on unused compute capacity, potentially leading to significant cost savings during off-peak hours or for less time-critical tasks. Spot instances empower users with tighter budgets or fluctuating workloads to strategically leverage lower-cost resources for development, experimentation, and background tasks. This frees up on-demand instances for truly time-sensitive work.
I would like more documentation about edge and advanced use cases. The official Docker images are only based on Debian: I would like to find official Docker images also based on other systems like Fedora or SUSE operative systems. It would be nice to have more hardware category options, like TPU coprocessors or ARM64 CPUs. I would like a pricing plan associated with a dedicated serverless platform specifically tailored to machine learning inference. It would be nice to create a custom serverless API system using my own custom machine-learning model.
I'm anxiously asking (and waiting for) Scala Spark Kernel, also the GPU offering can be improved with test offerings, like a spot instance for heavy testing. Kubernetes Clustering would be really interesting. We'd like a Spark Cluster as well. Public Clouds integration and sandbox environments would also be a true game changer. I would use it for cybersecurity forensics, paying one invoice for a test environment and cybersecurity testing/validation. Crontab jobs would also make this service much more interesting for the corporate market.
Saturn Cloud should include prebuilt images for advanced data science packages like LightGBM in the next release. If possible, they should also provide a Kaggle image, which contains the most common Python packages used in machine learning. I would like the option to check or uncheck the data science subpackages we need in the environment. Usage reporting should be more precise and quantify use in the number of minutes instead of just hours.
We'd like to have the capability for installing more libraries. I can rewrite my code to process images in Sklearn and Marplotlib. However, it would be much nicer if you provide a wider range of libraries. The need to give more language support (C++). Some models give different results in different implementations which can be a headache since you don't see the same results for the same hyperparameters. They should provide a way to host API/web apps that is easy and free. It is very common for data scientists to deploy a web app for their machine learning models these days. Saturn Cloud should have this feature.
Saturn Cloud is a platform optimized for machine learning tasks with tools for distributed computing and resource scalability. With its support for multiple programming languages and libraries, it provides an environment conducive to experimentation and prototyping.Saturn Cloud offers a high-performance computing experience with Dask cluster support, facilitating distributed computing and resource scaling. The integration with Jupyter environments allows seamless transitioning for users...
My main suggestion for improvement centers on pricing. Introducing a tier modelled after AWS spot instances would be a game-changer. Users could bid on unused compute capacity, potentially leading to significant cost savings during off-peak hours or for less time-critical tasks. Spot instances empower users with tighter budgets or fluctuating workloads to strategically leverage lower-cost resources for development, experimentation, and background tasks. This frees up on-demand instances for truly time-sensitive work.
I would like more documentation about edge and advanced use cases. The official Docker images are only based on Debian: I would like to find official Docker images also based on other systems like Fedora or SUSE operative systems. It would be nice to have more hardware category options, like TPU coprocessors or ARM64 CPUs. I would like a pricing plan associated with a dedicated serverless platform specifically tailored to machine learning inference. It would be nice to create a custom serverless API system using my own custom machine-learning model.
I'm anxiously asking (and waiting for) Scala Spark Kernel, also the GPU offering can be improved with test offerings, like a spot instance for heavy testing. Kubernetes Clustering would be really interesting. We'd like a Spark Cluster as well. Public Clouds integration and sandbox environments would also be a true game changer. I would use it for cybersecurity forensics, paying one invoice for a test environment and cybersecurity testing/validation. Crontab jobs would also make this service much more interesting for the corporate market.
Saturn Cloud should include prebuilt images for advanced data science packages like LightGBM in the next release. If possible, they should also provide a Kaggle image, which contains the most common Python packages used in machine learning. I would like the option to check or uncheck the data science subpackages we need in the environment. Usage reporting should be more precise and quantify use in the number of minutes instead of just hours.
We'd like to have the capability for installing more libraries. I can rewrite my code to process images in Sklearn and Marplotlib. However, it would be much nicer if you provide a wider range of libraries. The need to give more language support (C++). Some models give different results in different implementations which can be a headache since you don't see the same results for the same hyperparameters. They should provide a way to host API/web apps that is easy and free. It is very common for data scientists to deploy a web app for their machine learning models these days. Saturn Cloud should have this feature.