Fast Setup, Easy to Use
I used Saturn Cloud for a Machine Learning project that trained a network intrusion classier using PCAP data. In a few minutes I was coding in a jupyter notebook without having to worry about data privacy, and collaboration was simple. The ease of setup and computational power available make this a great collaboration tool, and I will definitely be using again.
Saturn Cloud provides a very intuitive and user-friendly web based Jupyter notebook (and Jupyter Lab) interface on top of Dask (and RAPIDS), an open source Python library for parallel computing. I used Saturn Cloud to work on a fairly large Dask based NLP project using Jupyter notebooks. Prior to that, I was building my NLP pipeline on a single AWS EC2 (t2.2xlarge) instance, but the workload soon proved too large. Saturn Cloud provided an (almost seamlessly) way to scale up and complete my jobs -- the changes involved reading and writing directly from and to S3 instead of local disk, and switching out the Dask distributed scheduler with the Saturn Cloud one. This gave me access to a cluster of AWS EC2 machines and I was able to complete my jobs without resource issues, and within reasonable time. I would also like to thank Saturn Cloud engineers for their timely and effective help, without which the project would probably have taken much longer. As someone with an Apache Spark background, I found the Dask programming model to be lighter weight and more extensible, and the Saturn Cloud platform provides a simple, powerful, and intuitive way to leverage it.