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How Is Machine Learning Transforming Software Development Practices?

Machine Learning and Artificial Intelligence are probably the hottest buzzwords doing the rounds in recent times. As far as expert perceptions go, analysts and experts are anticipating a significant increase in the adoption of AI and its related fields, in tackling complex and meaningful cases and problems. And this applies to the field of software development as well.

Inherent complexities mixed with sub-optimal practices often make the entire task of software development tough and therefore businesses are considering exploring the deepest secrets of Machine Learning (ML). Experts from the top software development company in Californiasay that as per reports, the market for software development is predicted to jump from $429 billion in 2017 to about $507 billion in 2021. (Source)


The very fact that almost 40% of global organizations plan to integrate AI solutions in their existing software framework by the end of 2020, is a clear indication of how ML and AI have already made their way into the core of enterprises. (Source)

What is Machine Learning?

Now, before getting into how ML works in the favor of software development, let us try to get a clear fundamental understanding of what ML exactly is.


Built along some of the effective principles of AI, ML is an advanced technology that can design computer software having learning abilities and therefore the system improves over time with thorough learning. But contrary to the popular opinion there is not any programming involved and instead, ML employs algorithms to train and teach the software helping it to learn eventually.

These algorithms are designed by experts in a way so that it can take in large piles of data and analyze it and based upon the results, it can detect patterns and trends. This, in turn, helps the software in making accurate predictions eventually improving its decision-making abilities.


How Machine Learning is helping us in our everyday lives?

Often the common people would not realize the fact that they are making great use of ML in the following ways:

· When you ask your devices to perform some tasks for you without even touching your phones and tablets.


· Super-smart algorithms overwhelming you with suggestions of what to buy and which movie to watch are also a product of ML.

· Self-driving cars have also been developed using the fundamentals of ML and AI.


· The Gmail auto fill up and follow-up features are also been created by borrowing ideas from ML.

How ML works in developing software?

As mentioned before, ML works by implementing various algorithms and so the types of algorithms and their modus operandi are illustrated below:

· Complete supervised learning

The algorithms responsible for it guide a computer system by providing in all of the input and output data. In the case of supervised learning, the algorithms not only a set of input data but also a few of the known or received feedbacks to questions.


The algorithms are then made to function by the techniques of regression and classification and their purpose is to improve predictive modeling.

· Complete unsupervised learning

Contrary to the previous algorithms, this one feeds on to the set of only those whose responses are still unknown. As there are no previous responses found, these algorithms intend to train the software to look for hidden patterns across an unknown data set.


The unsupervised type learning algorithms are being widely used in banks to detect any suspicious transaction. But in case you are considering using ML to improve the software development of your enterprise then you would find its use in descriptive modeling.

· Semi-supervised learning

If the real-world is to be considered, then both labeled and unlabelled data sets are found in abundance. That is why, only using supervised or unsupervised learning would not do much good, and therefore ML needs to apply a fusion of these both. Semi-supervised learning algorithms are perhaps the most important algorithms because of its


· Reinforced learning

These algorithms differ quite a lot from the above-mentioned ones as these algorithms train the computer system by trial and error procedures. In this case, also, a feedback loop plays an integral part in the process because the system learns from past experiences and trends.


If you have ever played computer games like chess then you should know that those games are born as a result of Q-learning by implementing reinforced learning algorithms.

How can businesses derive maximum from ML?

If you are willing to know more about ML and its contribution to the world of businesses, then here are some well-proven examples for you.


· Utilizing IPA

IPA stands for Intelligent Process Automation and it makes all the business tasks simpler by making them automated like data entry jobs. Also, it can automate more complex and critical job roles such as insurance risk assessment. To get the most of IPA, businesses will have to adopt the practices of ML.


· Optimizing sales and marketing

The sales function in any business not only tops the priority list but it also gathers a lot of data. Therefore, you can feed this data to the ML algorithms and it will get you amazing ideas for marketing and sales, for example, intelligent ad placement.


· Employing virtual assistants

Virtual assistants powered with ML can now solve the umpteenth queries or problems of your customers. They can also offer intelligent solutions to those problems as the system can learn from customer engagement data. As the ML would be taking up the easier and obvious questions, your customer support team would be free to resolve more complex queries.


· Strengthening cybersecurity

If the domain of your business is cybersecurity and you are looking for related software development, then ML would be a great help to you in building cybersecurity software. By applying predictive modeling, your software would be able to detect threats at the earliest while notifying you about the same. As Ml would aid businesses with large sets of data, you as a business leader would have the option of analyzing the data generated from mobile and other IoT devices and scrutinizing it for threats by cyber-attackers.

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