One of the major challenges the IoT industry has to face today is how to manage and make sense of the myriad of data generated by the billions connected devices and processes forecasted by the end of the decade. While for years traditional statistical tools for data modeling and analysis have served companies and organizations by merely providing a description of the events captured in the data, recent advancements in technologies like machine learning (ML) and artificial intelligence (AI) are opening the road to more advanced analytics techniques that can predict future outcomes based on collected data, and offer suggestions on how to prevent, mitigate, or ensure that outcome. Today, as IoT-based technologies and solutions are increasingly being adopted in industrial and business processes, more and more companies are leveraging machine learning technologies for data analytics.
In this research analysis, ABI Research analyzes the topic of machine learning related to its use in IoT systems and applications. It introduces the concept of machine learning, describing the main types of algorithms in use today and their most common applications, and also provides an overview of the role of machine learning in the IoT space, with particular focus on IoT data analytics and a wrap-up of the main market and technology trends within this area. The report contains an overview of the key verticals and applications, as well as the annual revenue forecasts of the IoT analytics market for the 2015 to 2021 timeframe, broken down into ML-based and non-ML-based IoT analytics, and it presents the profiles of a number of relevant vendors that provide machine learning capabilities within an IoT data analytics context.