Amazon Web Services recently announced the availability of machine learning technology that allows developers to create predictive analytics by using the same algorithms that Amazon uses to manage its supply chain inventory and operations. Amazon’s Machine Learning platform empowers developers and data scientists with the ability to identify patterns and predictive analytics for data stored in Amazon S3, Amazon RedShift and Amazon Relational Database Service. By using Amazon Machine Learning, developers can obtain analytic insights without writing custom-built predictive analytics-based applications that require complicated scripting, debugging, code deployment and application management. In addition to enjoying the benefits of preconfigured machine learning libraries and wizards that accelerate access to pattern recognition and predictive analytics, Amazon Machine Learning enables customers to enjoy the benefits of a scalable platform capable of generating billions of predictions per day in real-time. Popular use cases for Amazon’s Machine Learning technology include the detection of fraud, the ability to personalize web-related content and deliver targeted marketing campaigns that iteratively become more effective in conjunction with the evolving sophistication of the predictive model. Amazon’s Machine Learning platform competes directly with Microsoft Azure’s Machine Learning platform that was released in public preview in July 2014. By rendering available the same technologies used by Amazon’s data scientists, Amazon adds yet another incentive for customers to leverage its ever expanding portfolio of cloud and big data products and services. The increasing availability of machine learning technologies underscores the democratization of analytics enabled by the contemporary cloud and big data revolution, even though many of the solutions available on the market remain proprietary and attached to usage of a larger IaaS platform.