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What Developers Need to Know About Machine Learning in the SDLC

Machine learning

To learn about the current and future state of machine learning (ML) in software development, we gathered insights from IT professionals from 16 solution providers. We asked, "What do developers need to keep in mind when using machine learning in the SDLC?" Here's what we learned:

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Fundamentals

The biggest issue for ML is viewing it as an omnipotent savior of the SDLC, thereby negating the need to adhere to traditional SDLC design and protocol. ML can greatly improve efficiency and allow developers to better allocate their time to actions that require human input. It cannot, however, completely take the place of conscientious, diligent and thoughtful software planning, design, development, and version control.

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Tips for How to Create an AI App for Your Business

We are entering the age of “Software 2.0,” where artificial neural networks (ANN) are already in use and appreciated by those who are from a development background. Even, there, however, technologies like artificial intelligence, deep learning, machine learning, and advanced analytics changing the way developers create intelligent software entities through computers and in collaboration with human intelligence.

Today all of the smartphones, smart TVs, cars, and video games use artificial intelligence. Like you can use Siri to give you directions to the nearest petrol pump. Tesla is using AI and big data to make the idea of self-driving vehicles into reality. According to a post published in Fortune, AI can now read our thoughts and convert them to images by interpreting brain signals.