If you are a meticulous developer or you are learning the basics of machine learning, you are going to love our new sandbox feature, inline sources.
As you might know, BigML comes with a free Machine Learning Sandbox, a development mode that you can switch on/off in your account settings. It lets you do everything that you want to do in the production mode for free but with the limitation of 1 MB maximum for datasets, models, and evaluations.
While in development mode, we charge you no credits for using BigML. This way you can focus on developing and testing your application, while not spending yourself into the poor house.
While developing and testing applications, you may want to manually create input data to simulate specific situations. To that end we have added an inline editor. Now you can create inline sources in BigML by manually inputting any data you like. Of course you can also copy/paste the content of an existing file and edit that data.
So how does it work? As always: with almost shocking simplicity! Make sure you are in Development mode first. Then go to the Source-tab of your dashboard. Here you’ll see an icon for creating an inline Source.
Clicking that icon opens up the editor. Now you can create, paste, edit all you want to create your new inline source. Make sure you give it an appropriate name, hit ‘Create’ and you are good to go!
Inline sources might also be useful to help you understand how machine learning works in general and how BigML’s algorithm specifically behaves. Imagine that you are taking an introductory class on learning with decision trees and want to check how different data alters how the trees are built. You can now easily do that in a matter of clicks. In fact, the example above is the party dataset taken from Steven Marsland introductory book to machine learning from an algorithm perspective. See below the corresponding model.
Inline sources is a simple feature that we trust will be helpful as you are developing great applications using BigML, or just learning more about how machine learning works.