I’ve written this post primarily for people who work with RapidMiner Studio on their laptops, but often feel that they need more: more resources to train larger and more accurate models, better hardware to run processes faster or more opportunities to deploy and publish their work.
I want to show you how easy it is now to leverage the Cloud resources from the major providers (Amazon AWS and Microsoft Azure) to get your own RapidMiner Server and extend your capabilities as much as you need.
RapidMiner Server: run anywhere
Both in Azure’s Marketplace and in AWS’s you will find virtual machines for RapidMiner Server. They include a pre-configured database, a Job Agent, a pre-defined default queue and the appropriate license: everything you need to start working, out of the box. See here for more details. You just need to click, select the hardware and network configuration and, in 2-3 minutes… presto! There it is: ready to use. You can copy the URL and use it to connect from your local Studio.
And, because it has its own license, you don’t need to worry about any limitation on memory or CPUs. You just need your Studio (non-Free) license to tap into all these resources.
Once it has started up, you get an execution environment as big as you need, you just have to choose the right VM that fits your use case. If you need large memory, lots of CPUs or even GPUs for your deep learning models, you can have it in a few clicks. When you are done, you can stop it and continue at any other time: you just pay for the time you’ve used it, it’s that simple.
But it’s not only execution that you get, the RapidMiner Server provides you with a repository for your work and to share your processes with other Data Scientists. And, what’s more important, it gives you a way to deploy and publish your processes so that your end users can securely connect to it and get the results.
Find your use case!
This is helpful in many situations, just to name a few:
If you need to train large models in high-memory VMs.
If you want to have a quick access to data that you already have in the Cloud.
If you want to deploy your solutions in an open environment, accessible by others.