Michael Firn’s piece on the most common approaches to machine learning for business problems was featured in GigaOM magazine. Machine learning is a hot topic among businesses across all industries. However, there is a danger of thinking about machine learning as one monolithic business solution. In fact, there are many forms of machine learning and each is capable of solving different sets of problems.
Michael’s piece explains supervised, unsupervised, semi-supervised and reinforcement learning as the most common approaches to machine learning applied to business problems. In going through each, the piece runs through practical applications of each technique, and how at Vidora, we’ve used these techniques to help Fortune 500 partners solve some of their most pressing problems in innovative ways.
Vidora enables anyone in any business to build and use complex machine learning models. With Vidora’s self-service platform, Cortex, machine learning is intuitive, interpretable and fast. Cortex also automates the entire machine learning pipeline from raw data to model outputs, and the Items API is an important component of that. Experts in machine learning and artificial intelligence from Stanford, Berkeley and Caltech developed Cortex. Finally, Cortex sits at the heart of some of the largest global brands, such as Walmart, News Corp, and Discovery.