Becoming an AI Engineer is not Rocket Science! But First, Check the Caveats
AI-trained people are confused between AI engineering and machine learning or lack a proper understanding of the scope and challenges therein
Artificial Intelligence is everywhere in the form of a surreptitious avatar than as an obvious experience. And so are the artificial intelligence engineers. Very rarely a common person is aware of what it is like to be an AI engineer. Many times it so happens that even well-informed or AI-trained people are confused between AI engineering and machine learning or lack a proper understanding of the scope and challenges therein of different job roles in artificial intelligence. AI engineering will rule the tech world erasing the line between human intelligence and artificial intelligence. There will be no dearth of demand for them as the industry grows in the coming few decades. So, to understand if it is the role for you, it is very much necessary to get to the bottom of it. Let’s dive in.
The difference between artificial intelligence and machine learning
AI and ML are both part of computer science and most of the time they are used together because they are interrelated. But for someone who is specialized in designing AI or ML models, the differences are stark. Artificial intelligence is a broad term applied to all the techniques that make a system mimic human intelligence. Machine Learning is a subset of artificial intelligence. It helps AI systems to make predictions and take decisions without having to be explicitly programmed for the purpose. The machine learning algorithms learn by themselves using historical data.
What do Artificial Engineers do?
An AI engineer’s typical responsibilities include using programming algorithms to assemble, test, and convey AI models. They are troubleshooters with the responsibility to ensure the proper implementation of AI systems and the corresponding infrastructure. They build, deploy and maintain AI-based systems and develop the software for the systems after taking inputs from business leaders. Apart, they are also responsible for implementing Machine Learning (ML) processes as part of their job role. Natural Language Processing, building chatbots, building recommendation engines, and data pipelines are other major job responsibilities. Precisely, AI engineering is all about processing software engineering, data science, and automation to build functional AI systems.
The Dark Side of AI Engineering Career
Building an AI product is as difficult as building a software product. Particularly when you are not a software engineer, building real AI products will be difficult. AI is a dynamic field with every improvement being a technological specialization in itself. Nothing you learn today would probably be relevant in the next two years leaving you fumbling over the new technologies. This is the nature of AI in general and accepting it becomes one’s first responsibility. Image processing is another typical area, a seasoned AI engineer finds perfunctory. And finally, when the commercial point of a business is brought into the picture, it becomes an entirely different roller-coaster. The fact that you can build a product will not translate into a business idea unless it is economically viable and worth taking the risk. A certain idea might deliver the desired results but the cost overruns, millions of dollars, might outrun its very purpose, i.e., saving money.
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