The sheer volume and unstructured nature of the data generated by billions of connected devices and systems presents significant challenges for those in search of turning this data into insight. For many, machine learning holds the promise of not only structuring this vast amount of data but also to create true business intelligence that can be monetized and leveraged to guide decisions.
In the past, it wasn’t possible or practical to implement machine learning at such a large scale for a variety of reasons. Recently, three major advances have enabled more organizations to take advantage of machine learning to enhance business intelligence:
1) Bigger data (and more importantly, better labeled data)
2) Better hardware throughout datacenters and high performance computing clusters
3) Smarter algorithms that can take advantage of data at this scale and learn from it
Machine learning, generally speaking, refers to a class of algorithms that learn from data, uncover insights, and predict behavior without being explicitly programmed. Machine learning algorithms vary greatly depending on the goal of the enterprise and can include various algorithms targeting classification or anomaly detection, clustering of information, time series prediction such as video and speech and even state-action learning and decision making through the use of reinforcement learning. Ensembling, or combining various types of algorithms, is also common as researchers continue to push the state of the art and attempt to solve new problems. The machine learning arena moves very fast and algorithmic innovation is happening at a blistering pace.