The activities of many globally active IT corporations prove that machine learning will be high on their lists. Be it Google, IBM or Microsoft – all of them have made machine learning an important component of their business strategies. In addition, the tech giants have been recruiting entire competence teams and acquiring machine learning and AI startups. While IT, automotive, telecommunications and media are among the pioneers of this development, more traditional industries such as the chemicals sector, logistics/transportation and pharmaceuticals are already awaiting their turn. This makes me wonder whether machine learning can offer genuine value to the field of software development itself.
The phrase "Machine Learning" refers to the automatic detection of meaningful data by computing systems. In the last few decades, it has become a common tool in almost any task that needs to understand data from large data sets. One of the biggest application of machine learning technology is the search engine. Search engines learn how to provide the best results based on historic, trending, and relative data sets. When you look at anti-spam software, it learns how to filter email messages.
Can machine learning be used to accelerate the development of traditional software development lifecycle? As artificial intelligence and other techniques get increasingly deployed as key components of modern software systems, the hybridisation of AI and ML and the resultant software is inevitable. According to a research paper from the University of Gothenburg, AI and ML technologies are increasingly being componentised and can be more easily used and reused, even by non-experts. Recent breakthroughs in software engineering have helped AI capabilities to be effectively reused via RESTful APIs as automated cloud solutions.
Artificial intelligence is changing the world more rapidly than anyone could have predicted a few years ago. The explosion in available data, coupled with low-cost computing power and dramatic advances in AI capabilities, will enable organizations to optimize their operations, personalize their products, and anticipate future demand.
This video gives the reasons why using modern machine learning tools like H2O's Driverless AI on VMware virtual machines provides benefits to the data scientist - particularly in isolating different toolkits and platforms into sandboxes so that training work can be done in parallel and more efficiently.