by bill-s, 2018-12-28T13:42:52.165Z
Machine learning (ML) is being used in a wide range of applications, from autonomous cars and credit card fraud detection to predictive maintenance in manufacturing and beyond.
But there’s a problem. Building ML solutions is complex and requires highly skilled personnel with Ph.D.s in mathematics or other quantitative fields. The demand for data scientists has outpaced supply, inhibiting adoption of ML among enterprises. Many companies have vast stores of data, yet they’re unable to employ predictive analytics to improve business decision making and achieve success.
The automated ML capability in Azure Machine Learning is designed to overcome these obstacles and make AI more accessible to every developer and every organization. In this article, I’ll show how automated ML can be used to quickly build an energy demand forecasting solution.