Recent progress in machine learning has been driven both by the development of new learning algorithms and theory and by the ongoing explosion in the availability of online data and low-cost computation. The adoption of data-intensive machine-learning methods can be found throughout science, technology and commerce, leading to more evidence-based decision making across many walks of life, including healthcare, manufacturing education, financial modeling, policing, and marketing.
Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today’s most rapidly growing technical fields, lying at the intersection of computer science and statistics, and at the core of artificial intelligence and data science.
The platform and resources an organization uses to build the product and run the algorithms is determined by business goals. Universally the primary requirements of any machine learning system will include reliability, performance, and on-demand scalability.