<p>These days there's buzz among companies and enterprises to hop on to the IoT bandwagon. Almost every discussion will boil down to focusing on technology and maximising output by embracing IoT. According to Machina Research data IoT in India will account for $10-12 billion by 2020. This will comprise of $194 billion from hardware and $179 billion from software. However what if we told you that this progression was merely a gradual and expected phenomenon and we are yet to witness more fascinating capabilities of technology!<br><br>Till date, computers performed according to programs and applications that were created specifically for conducting a particular task. To cite a simple illustration, we are able copy images from the web and edit it to our taste by using platforms such as MS Picture Manager or Paint or Adobe Photo Shop. What if computers, like humans, began learning from experience? And this has already begun to take shape…<br><br>Machine Learning is the dawn of an exciting new era of info and computer science wherein computers can figure out how to perform important tasks by generalizing from examples.<br><br><strong>What is Machine Learning?</strong><br>As more data becomes available, more ambitious problems can be tackled. Machine learning is based on algorithms that can learn from data without relying on rules-based programming. In the past decade, digitization of information has led to a data explosion in both volume and complexity. While traditional computing frameworks have failed to provide adequate computing power for the now common data-intensive computing tasks, cloud computing provides an effective alternative to enhance computing power. Machine learning algorithms are powerful analytical methods that allow machines to recognize patterns and facilitate human learning. It came into its own as a scientific discipline in the late 1990s as steady advances in digitization and cheap computing power enabled data scientists to stop building finished models and instead train computers to do so.<br><br>Machine learning is linked to artificial intelligence, the development of computers with skills that traditionally would have required human intelligence, such as decision-making and visual perception. It is the part of artificial intelligence that actually works. You can use it to train computers to do things that are impossible to program in advance. Search engines like Google and Bing, Facebook and Apple's photo tagging application and Gmail's spam filtering are everyday examples of machine learning at work. The fundamental goal of machine learning is to generalize beyond the examples in the training set.<br><br>As with their learning-system forebears, the overhead of machine-learning systems is typically huge. But today we have the option to place these systems in the cloud. Amazon Web Services, for example, supports machine learning using AWS's algorithms to read native AWS data (such as RDS, Redshift, and S3). Google has supported predictive analysts for some time with its Google Prediction API, and Microsoft provides an Azure machine-learning service.<br><br>The ability to predict the future for both tactical and strategic purposes has eluded us because of prohibitive resource requirements. But today, thanks to the cloud for machine learning as a service, you can apply this technology far and wide on all that data enterprises have been collecting.<br><br><strong>Machine Learning vs. Artificial Learning</strong><br>One often believes machine learning to be synonymous with artificial intelligence but it isn't so. Artificial intelligence is a broad term referring to computers that are capable of essentially coming up with solutions to problems on their own. The information needed to get to the solution is coded and AI uses the data to come up with a solution.<br><br>On the other hand Machine Learning takes the process one step further. Machine learning is capable of generalizing information from large data sets, and then detects and extrapolates patterns in order to apply that information to new solutions and actions.<br><br>Machine Learning and Artificial Intelligence are highly inter-dependent fields that they need each other to analyze and perform activities.<br><br><strong>Machine Learning in Cloud Computing</strong><br>Machine Learning is a fully managed, on-demand, pay-as-you-go and easy to use service provided by prominent cloud providers like Amazon Web Services, Microsoft Azure and Google Cloud Platform. The cloud-based Machine Learning service gives business a chance to get started with Machine Learning and make valuable decisions.<br><br>Given the enormous growth of collected and available data in companies, industry and science, techniques for analyzing such data are becoming ever more important. Today, data to be analyzed are no longer restricted to sensor data and classical databases, but more and more include textual documents and web pages, spatial data, multimedia data, relational data.<br><br>Machine Learning is inherently a time consuming task, thus plenty of efforts were conducted to speed-up the execution time. Cloud computing paradigm and cloud providers turned out to be valuable alternatives to speed-up machine learning platforms. As a key service delivery platform, Cloud computing systems provide environments to enable resource sharing in terms of scalable infrastructures, middleware, application development platforms and value-added business applications.<br><br>Large players in IT have already been using machine learning internally. Microsoft is one example. The company has integrated machine learning technology into its cloud services to automatically tag users' photographs and to boost performance of the language translation facilities of its Skype service. It also offers a ready-to-use cloud platform to customers via its Azure Machine Learning services. An API lets them upload data (like big data and IoT/Internet of Things data) to feed machine learning programs and continually update the 'training' process to keep the output from their programs relevant and accurate.<br><br>The growth of the Internet of Things (IoT) has also been helped by the cloud, as apps and services need a central location to pump all of their data into before it can be analysed and utilised, as well as accessed and controlled.<br><br>The fall in storage prices and explosion of data created by new computing power have both been key factors in driving innovation, and thanks to its rapid increase, the cloud industry is really helping 'democratize' machine learning, making it available in more industries than ever before.<br>This will lead to more and more cloud providers offering machine learning as a service products and IoT management services to give users a way to centralize and make use of all their data.<br><br>The idea of a machine taking decisions as "deeply unsettling" - after all, there are increasing and understandable fears among many workers that eventually a machine will do their job and they'll be out of work. However the expansion of machine learning will actually bring benefits to employees.<br><br>The reality is that one can probably use the machines to get rid of all the tedious boring bits so that one can focus on more value-added objectives.<br><br><strong>Employing Machine Learning in Business</strong><br>Machine learning and big data will transform every industry as IT moves towards a cloud-based business. What it means is that one will be able to very quickly create a very powerful system without having to have employed those people who do things you don't understand; you simply take advantage of it. We'll start to see organisations starting to realize that put machines together in the same way that apps took off!<br><br>By ingesting data across all application and infrastructure domains, a machine learning-based event management system can not only detect events/alerts in real-time, but can also correlate and contextualize information so that it is meaningful to IT Operations. Machine learning is the only solution making it possible to correlate, contextualize and create clusters of related alerts known as "situations." Organisations must choose machine-learning industry-specific products over generic statistical tools.<br><br><em>The author, Harshad Mehendale, is consultant at BSIL - Blue Star Infotech Ltd</em></p>
