Before coming to the skillsets required to become a successful ML/AI engineer & derive a prospering career in this field; it is first important to understand the key requirements & nitty-gritties involved in machine learning or artificial intelligence projects.
Artificial intelligence is a broader concept which deals in instilling human intelligence to machines so as to enable them to self-process, self-learn, self-think and finally self-correct themselves. The key rationale behind the employment of artificial intelligence is to enable machines or systems to act in itself without the programmer having to explicitly code each and every business logic.
Machine learning, on the other hand, is an application of artificial intelligence – wherein logical algorithms & statistical models are designed & formulated to equip systems to self-learn from identifying data patterns & trends; and drawing inferences & conclusions from the same.
What are the skillsets required by AI/ML engineers to succeed in their careers?
A successful engineer in the field of artificial intelligence & machine learning needs to have an in-depth know-how of data science & statistics; and certain basic knowledge of data processing & software engineering. A few of the important skillsets required to be considered by professionals & aspirants in the field of AI/ML are mentioned below for reference –
- Practical or hands-on experience in data science & statistics – An important factor of consideration is that machine learning or artificial intelligence is not a theoretical or academic concept. Hence, going deep into research & understanding the theoretical nitty-gritty of statistical concepts is not a mandate for becoming a successful AI engineer
- Computer programming & fundamentals – In-depth knowledge of computer software fundamentals starting from data structures, trees, graphs, optimization algorithms, linear programming, and computer architecture is required; as the role of an AI engineer would be to simulate a machine to behave like a human. Hence, without understanding the working principle of systems, it would be difficult to cope
- Statistics & probability distribution – Statistics form the base of data science; which is an integral part of machine learning. ML engineers must be proficient in probability concepts including conditional probability, Bayesian principles, Markov models etc. In addition, they should also be aware of univariate as well as multivariate statistical analysis – which would form the base of machine learning techniques.
- Data modeling & model validation – Data modeling techniques (an offshoot of statistics) are extensively used in machine learning to identify valid patterns & classifications on datasets.
- Design & software engineering – Although this skill may seem the most irrelevant for a machine learning or artificial intelligence engineer; still professionals or aspirants must be cognizant of the fact that the ultimate product would be a software. Hence, basic knowledge of system design & deployment is a mandate for being a successful ML/AI engineer.