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Machine Learning In Cloud: Tools To Harness Artificial Intelligence

(Image Credits: mysliderule.com)
(Image Credits: mysliderule.com)

One has often wondered about the marvels of technology. Its speed, precision, uniformity and ease in accessibility has transcended from age-old PC’s to the contemporary hand held devices. 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!

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 them 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…

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.

What is Machine Learning?

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. 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.

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.

Two facets of mechanization should be acknowledged when considering machine learning in broad terms. Firstly, it is intended that the classification and prediction tasks can be accomplished by a suitably programmed computing machine. Secondly, it is intended that the creation of the classifier should itself be highly mechanized, and should not involve too much human input.

Machine Learning vs. Artificial Learning

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.

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.

Machine Learning and Artificial Intelligence are highly inter-dependent fields that they need each other to analyze and perform activities.

Machine Learning in Cloud Computing

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.

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

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.

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.
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.

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.

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.

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.

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.

Machine Learning Apps



Employing Machine Learning in Business

The dynamic nature of the market is a major hurdle for senior management of organizations across verticals. The diversifying trends and evolving consumer preferences are compelling organizations to rely on technology to understand and analyze market situations more accurately.

Today’s cutting-edge technology already allows businesses not only to look at their historical data but also to predict behavior or outcomes in the future. Machine learning is a critical tool used for gaining actionable insight into ever increasing amounts of data. The most common application of machine learning tools is to make predictions and find solutions for problems in a business:

  • Making customized recommendations for customers
  • Anticipating the future performance of employees
  • Forecasting customer loyalty

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!


About The Author :

This article is contributed by Mr. Harshad Mehendale, Consultant at Blue Star Infotech.An Indian listed company and part of US$700M Blue Star Group, Blue Star Infotech offers specialized IT services in the space of Mobility, Cloud Computing, Analytics & Business Intelligence across 7 industry verticals, Manufacturing industry being one of them. The company has completed more than 800 major IT projects on a wide range of hardware and software platforms and has delivered more than 1600 product releases for its technology customers worldwide.

(Disclaimer: This is a guest article contributed by the above mentioned author on Techstory. Techstory is not responsible or liable for any information mentioned in the article.)