Users are always the most valuable asset of an enterprise, which requires collaboration, creativity, and creativity. Machine learning will simplify the process and enable companies to understand how these ideas will be generated, replicated, and executed. According to Forbes' survey, by 2020, the value of the global IoT market will reach $457 billion. Manufacturing, transportation and logistics will have a particularly large market share, but more companies may soon be able to share the benefits of the IoT revolution. Research firm Gartner believes that by 2020, more than 65% of companies will adopt IoT products. Companies can choose to get a lot of data through IoT products, but how do they effectively use this data to drive business growth? Machine learning can prove to be a particularly effective solution to achieve this growth. Although machine learning is a complex artificial intelligence, it is not a new computing phenomenon. The term “machine learning†was actually created in the late 1950s, however, the use of machine learning to optimize business processes has long exceeded the practical capabilities of many companies. This is mainly due to the complexity of machine learning algorithms compared to algorithms used in traditional computers. Under normal circumstances, the computer will solve some problems because it has been specially programmed. However, machine learning algorithms have some differences in using large amounts of data to guide their decisions and predictions. For example, software for email can make a more meaningful classification of spam by selecting phrases that are commonly used in spam. At the same time, Netflix can recommend new movies and shows through content previously purchased by users. These are all direct examples of machine learning. Traditionally, businesses interested in using machine learning have been limited by the cost of supplying and maintaining computers and storage devices for hosting and executing machine learning algorithms. However, due to the nature of cloud computing and technological advances, launching these algorithms has become a more viable option. Today, many organizations can easily take advantage of cloud computing solutions that enable them to scale computing and storage to meet their machine learning needs. These services also benefit from high-performance computing services that can remain within their acceptable cost range due to the available pay-per-view subscription model. As some media have described this situation, “Cloud computing will be an ideal replacement for machine learning to rejuvenate.†Although machine learning has many merits to be praised, the effectiveness of its algorithms still depends heavily on the input data. A large amount of relevant data can drive the development of machine learning algorithms, just as useful clues can help detectives come to more sensible conclusions. It is for this reason that the Internet of Things can provide an ideal use case for this technology. A wide variety of IoT devices can generate data very frequently and then put that data into machine learning algorithms. For example, the information provided by a company's critical equipment can help it (or more accurately machine learning) anticipate the possible failures of these devices or how long they may run. These revelations can help companies save on maintenance time. The transportation industry and the logistics industry will also be attracted to machine learning. This is because machine learning can get a lot of data from the vehicle to help improve this security and reliability. Of course, in order to maximize the efficiency of machine learning, companies need sufficient cloud computing access. Fortunately, the public cloud will be widely developed in the enterprise field. According to a survey of people in the IT industry, about 37% of respondents said their workload will be running in the local data center, but by 2020, this percentage may drop to about 27%. At the same time, about 31% of respondents said they use a public cloud to carry their workload; however, this number will rise to about 41% by 2020. The use of private and hybrid clouds is expected to increase during this time. Companies can also draw inspiration from the IoT examples highlighted by these noteworthy cloud solution providers. Small Size Barcode Scanner Engine Small Size Barcode Scanner Engine ,Se4850 Scan Engine,Oem Scan Engine,2D Barcode Scan Engine SUNLUX IOT Technology (Guangdong) INC. , http://www.sunluxbarcodereader.com