Open source refers to something people can modify and share because they are accessible to everyone. You can use the work in new ways, integrate it into a larger project, or find a new work based on the original. Open source promotes the free exchange of ideas within a community to build creative and technological innovations or ideas. It helps you to write cleaner code. That can be of any choice.

One of the exciting developments in machine learning recently is the rapid emergence of a new class of model servers. Model servers simplify the task of deploying machine learning at scale, the same way app servers simplify the task of delivering a web app or API to end users. The rise of model servers, coupled with increasingly interoperable models, will likely accelerate the adoption of user-facing machine learning in the wild. Although there has been an abundance of open source machine learning software, much of the ecosystem has been focused on model-building. The large Internet companies have built their own model serving infrastructure (such as FBLearner Predictor and Michelangelo), but there have been few easy options for the rest of us.

Deepdetect is a young open source deep-learning server and API designed to help in bridging the gap toward machine learning as a commodity. It originates from a series of applications built for a handful of large corporations and small startups. It has support for Caffe, one of the most appreciated libraries for deep learning, and it easily connects to a range of sources and sinks. This enables deep learning to fit into existing stacks and applications with reduced effort. Machine learning is the next expected commodity on the developer's stack.