The first step to a successful ML project is to understand that these projects require different processes, terminology, workflows, and tools than those needed by traditional development.

Learn how to organize and structure your machine learning projects for real-world deployment. From directory layout to model ...

Mohanty studied using online resources, entered hackathons, and volunteered himself at work for ML projects. This as-told-to essay is based on a conversation with Suvendu Mohanty, a 37-year-old ...

MTRI scientists are active in developing state-of-the-art machine learning and statistical techniques for understanding natural and man-made environments. Projects Model Inventory of Roadway Elements ...

Apple's Machine Learning projects are added to the company's devices to provide support in the daily lives of its users with disabilities.

The flexibility, agility and ultimate cost of machine learning projects can be significantly impacted by data logistics and dependencies, according to Jim Scott, VP, Enterprise Architecture, at MapR.

DataRobot is rolling out templates for common machine learning use cases to reduce the amount of work involved in building AI applications.