Impact of Machine Learning on Software Development
It has been ascertained that Artificial Intelligence is increasingly being deployed as a key component of modern software systems. Facial recognition cameras, automated customer support, smart email categorization, and business forecasting are just a few of them. This is just the beginning and there is still long distance to cover. However, Artificial Intelligence wouldn’t have been possible without Machine Learning. These are two different concepts that are related to each other.
Machine Learning is the feeding of massive pieces of data into a computer system or machine for it to learn about them.
Artificial Intelligence is enabling the computer or machine to think on its own so that it can utilize and implement whatever it has learned in the Machine learning stage.
Technology is getting more advanced every day and it will be futile to ignore the benefits that Machine Learning and Artificial Intelligence can give to software development. Recent breakthroughs in software engineering have enabled AI’s capabilities to be utilized through RESTful APIs as automated cloud solutions. The existence of massive data sets in a computer system through machine learning can save a lot of time in researching and collecting this information.
These data inputs can include a client’s requirements, full-fledged knowledge of the latest software development tools available, the proposed timeline, number of resources, their qualifications, and more. Depositing all this information in a single system can increase efficiency of software development processes. Besides, Artificial intelligence can automate the process. There will be no space left for unpredicted instances that include human errors, wrong use of technology and system failures, or external factors such as lack of funds and absence of staff.
How can Machine Learning improve software development?
Replacement of Code: Machine Learning can make coding more efficient. Along with artificial intelligence, it can replace coders and developers by automating the brick laying process of software development. The advantage of using machine learning in the coding process is that the code need not be rewritten each time there are changes in the programming language. The neural network or the computational system that features the code can be trained to gather information of new coding programs as they are released.
Data Storage: Traditional databases and indexes can be outperformed by Machine Learning. Systems can actually predict where data is stored or if the data exists through Machine Learning. Besides, it requires much lesser data storage space and can facilitate faster processing of codes. Machine Learning can also be used to diagnose and provide quicker responses to problems such as running out of memory in a Hadoop cluster.
Automated Analysis: Machine Learning enables designing of tools that will enable the performance of more advanced data analytics. Exploratory data analysis can be automated along with statistical models for time series data and detailed explanations. Machine Learning can enable recognizing vulnerabilities in software. Systems can be enabled through machine learning over the code and look for known flaws.
Data Security: Cybersecurity can be enhanced and threats can be predicted on a massive scale with Machine Learning. It can avoid false notifications, delayed warning, and alerts. Machine Learning can enable a system to collect data from a combination of software installed in customer terminals and network sensors. This collected data is then analyzed through machine learning to differentiate normal behaviors and irregularities. The adoption of Machine Learning can help an organization quickly observe breaches in security.
SGS has expertise in designing Machine Learning enabled software solutions for different domains. Get to us for a quick discussion so that we can analyze the most apt solution for your firm.