"I got my Master's in Computer Science in 2011, and like others, I got tracked into coding as a software engineer. I started my career as a Java engineer developing software applications. Six or seven years later, I came across the profile of machine learning. Machine learning was not in a boom at that moment. The projects we got were almost always software engineering; machine learning projects were really, really hard to get."
"The college education system also hadn't properly adopted AI and machine learning in its curriculum. When you're doing a master's degree you can take a specialization, or you are doing a Ph.D. But what about the millions of software engineers who are early in their career and are not formally trained? The first thing I did was take some interest in learning what machine learning is. I grabbed some online free resources and joined hackathon projects."
Suvendu Mohanty earned a Master's in Computer Science in 2011 and began his career as a Java software engineer. Several years later he encountered machine learning, but ML projects were scarce and academic curricula had not widely adopted AI or ML. Many early-career software engineers lacked formal ML training. Mohanty learned through free online resources, joined hackathons, and volunteered for ML work at his job. Managerial attention led to on-the-job ML exposure. Initially challenging ideas and misconceptions required stepping out of a comfort zone. He concluded that machine learning is another stream within software engineering and now ML is booming.
Read at Business Insider
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