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The Essential Skills Developers Need in the Era of Machine Learning & AI

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The 5 Essential Skills Developers Need in the Era of Machine Learning & AI


Why is AI Relevant Today?

The swift growth of artificial intelligence in the last few decades has spun an interesting debate. Many people think that with the speed at which the field of artificial intelligence is progressing, plenty of technical and non-technical jobs might become redundant. On the other hand, the remaining population believes that AI is supposed to make our lives easier and cannot replace the human effect. Despite the contradictory views, the AI industry is booming. 


What Does an AI Engineer Do?

Before you jump on to the process of how to become an AI engineer, it is essential to know about the role and responsibilities. It will help you understand whether a career in artificial intelligence is the right fit. The following are the key responsibilities of an AI engineer:


  • Building AI models intelligence models with the assistance of AI calculations to procure important business bits of knowledge
  • Performing measurable investigation and utilizing understanding methods to smooth out authoritative cycles
  • Creating, programming, and preparing organizations to make AI models intelligence models that address complex undertakings
  • Creating and maintaining AI infrastructure


How to Become an AI Engineer?


1. Domain Knowledge

If you want to work in Artificial Intelligence, you must have knowledge of Neural Networks, Machine Learning, Deep Learning and other related fields. Domain knowledge helps in better understanding of the sector as well as understanding the risks and difficulties that need to be addressed. It will also help you in developing new techniques to deal with such issues. You can use your expertise to develop AI-enabled technology and services that will improve people's lives and enterprises. 


2. Programming Languages

Every AI expert should have a good knowledge of programming languages like Java, R, Python, C++, JavaScript and others. You should be able to develop code to suit your specific needs and use-cases. It will also be beneficial if you have a thorough understanding of computer architecture, optimization methods, data structures, graphs, trees, and other related topics. It's also a plus if you can speak more than one language because you'll be able to offer more, as companies value workers with diverse talents.   


3. Mathematical Knowledge

Algorithms and applied mathematics are heavily used by AI experts. That is why, to tackle AI issues efficiently, you must have great analytical and problem-solving abilities along with a mathematical understanding. Linear algebra, probability, statistics, graphing, optimization methods, and other mathematical abilities are desirable. These capabilities can be used to solve issues and create algorithms based on specifications.


4. Machine Learning

Machine learning is the study of computer-based algorithms that can learn and improve themselves through data and experiences. To make decisions and make predictions, ML algorithms develop models based on a specific data sample, known as training data. Artificial Intelligence requires ML understanding as AI allows the machine or system to behave in a smart way. ML is a method of making a computer intelligent and using that intelligence in real-time activities and issues.  


5. Deep Learning

Deep learning is an area of ML and data science that mimics the way people learn. It uses multiple layers to extract deep features from the sound or picture, as well as predictive analysis and statistics. The details will get more complicated with more levels.

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The Essential Skills Developers Need in the Era of Machine Learning & AI