DZone

This blog post, the fifth of our series of six posts about Deepnets, focuses o users who want to automate their machine learning workflows using programming languages. If you follow the BigML blog, you may know of WhizzML, BigML’s domain-specific language for automating machine learning workflows, implementing high-level machine learning algorithms, and easily sharing them with others. WhizzML helps developers to create machine learning workflows and execute them entirely in the cloud. This avoids network problems, memory issues, and lack of computing capacity while taking full advantage of WhizzML’s built-in parallelization. If you are not familiar with WhizzML yet, we recommend that you read the series of posts we published this summer about how to create WhizzML scripts (here’s Part 1, Part 2 and Part 3) to quickly discover their benefits.

In addition, in order to easily automate the use of BigML’s machine learning resources, we maintain a set of bindings that allow users to work in their favorite language (Java, C#, PHP, Swift, and others) with the BigML platform.

Source: DZone