Healthcare is an industry that keeps up with the times as well. With the amount of data generated for each patient, machine learning algorithms in healthcare have great potential. Where Machine learning (ML) is a subclass of artificial intelligence technology, where algorithms process large data sets to detect patterns, learn from them, and execute tasks autonomously without being instructed on exactly how to address the problem. Machine learning techniques can be applied to solve a wide variety of tasks. Classification -- machine learning algorithms can help to determine and label the kind of disease or medical case you're dealing with; Recommendations -- machine learning algorithms can offer necessary medical information without the need to actively search for it.

Use of Amazon Web Services' (AWS) Machine Learning (ML) – which lets developers add intelligence to applications and ramp up analytics – has more than doubled in the past year, the company said late Thursday. The 250 percent increase was driven by "broad adoption" of SageMaker, its end-to-end managed ML service, which was released in November, Amazon said. FICO, GE Healthcare, Grammarly, KloudGin, Lau Brothers, Limbik, Lionbridge, NFL, VMware, and more were among the new adopters cited by the company in its Q1 results. The figures come as companies scramble into the ML/AI space. Yesterday Microsoft customer Thoughtonomy, for example, announced the release of an enhanced AI functionality within its Software as a Service platform, which operates on Microsoft Azure.