Amazon, Google, IBM and Microsoft lead machine-learning charge
August 4, 2016
Amazon, Google, IBM and Microsoft are investing heavily in machine-learning technologies for data analytics, which could lead to machine-learning-based data analytics tools and services revenues to hit nearly $20bn in 2021, according to ABI Research.
This will be boosted, said the research company, as machine-learning-as-a-service (MLaaS) models take off.
"The emergence of the MLaaS model is good news for the market, as it cuts down the complexity and time required to implement machine learning and thus opens the doors to an increase in its adoption level, especially in the small-to-medium business sector," says Eugenio Pasqua, research analyst at ABI Research. "Most companies also offer a free trial period to allow customers to test the functionalities before fully committing."
Machine learning is the method of choice for developing software for disciplines such as natural language processing, speech recognition, computer vision and robot control. While in the past, businesses used it to tackle application-specific problems, today it is thought to be a building block of a larger system, with many companies developing more general-purpose tools that can be leveraged across an array of different industries and use cases.
Major cloud-infrastructure providers are investing in the technologies either to acquire or build upon their own machine learning expertise. These providers can provide these capabilities in a cloud-based delivery model, exploiting their own cloud infrastructures to offer APIs that developers can use to embed machine-learning capabilities quickly and easily into their applications.
"Solutions built on machine learning automate the IoT data modelling process, removing the labour-intensive and circuitous activities of model selection, coding and validation," said Ryan Martin, senior analyst at ABI Research. "This write once, run anywhere mentality has already seen big buy-in from companies like Amazon, Google, IBM and Microsoft to make advanced analytics more accessible to a broader and more evenly-distributed workforce. This will open the road for widespread adoption of machine learning."
• Six out of ten global operators, enterprises and analysts believe that today’s cellular networks are not fully prepared to support the IoT, according to a survey by Swiss M2M and IoT services company Starhome Mach. And 95 per cent ranked visibility of IoT devices in the network as important to monetise information and increase profits.