5 Skills You Need to Become a Machine Learning (ML) Engineer
If you have come to this article, that means either the career prospect of being ML Engineer fascinates you or your decision as to whether you should go for this option or not hangs in the balance, and hence, you are reading this to clear up your doubts. You might have a few questions such as:
- How is this career going to define me as a person?
- What changes will it bring to my life?
- How does it make me stand out from billions of people?
- Is this really a dream career as I do expect?
The fact that this field of artificial intelligence brings endless opportunities for doing good to people might help motivate you a little bit. Not only that, the scope for ML Engineering is going to burgeon a few years down the line, and if this is going to happen, one thing is certain that the average income of a well-qualified ML Engineer is going to shoot up. More importantly, the application of ML across industries is growing day by day. Seeing that, the demand of such professionals will rise exponentially, and an ML Engineer will automatically earn a high standing in society.
However, if such a career prospect is offering so much to an aspirant, it must not take anybody by surprise if it asks for some special skills. Before setting out on such a hardworking journey, one must know what skills are necessary to fit in this role.
1. Basic Qualifications
Without basic qualifications, you cannot go longer. It’s absolutely necessary to increase the probability of success. Become a graduate in Computer Science or Electronic and Telecommunications. Be that as it may, it is not compulsory because many Data Scientists and ML Engineers from the recent past did not belong to educational backgrounds of this sort. Graduates from other disciplines have proved their mettle in ML.
The most important requirements are the technical know-how to Python, C, C++, Java, or R. If you hold a certification of programming in one of the previously mentioned languages, besides your graduation or post-graduation, you are likely to receive more interview calls for the job of ML Engineer. With special libraries named SciPy and NumPy, in addition to relatively easy syntax, Python is of great help in ML.
The C language serves as a basis for the time series simulation and regression analysis, which can be useful in developing advanced algorithms. If you are conversant with the C language, then you don’t even need a PhD. Although the R language has wider academic usage, industries are adopting this language as it provides relatively simpler statistical visualization of algorithms. Hence, if you are suitably qualified to use R, then you surely will experience more chances of getting hired as an ML Engineer because this language, in combination with lexical scooping, assists engineers with delivering versatility in statistical models.
2. An Educational Background in Mathematics and Computational Science
Probability is the prediction of future events and statistics is the use of statistical models to deal with those future events. Without a doubt, these two branches, in combination with each other, serve a key role in ML. For example, Hidden Markov Models, Naive Bayes, Gaussian Mixture Models, conditional probability, likelihood, confusion matrices, p-values, and other statistical modelling procedures are essential for ML algorithms. Thus, operational knowledge of these branches will surely help you in being an ML Engineer.
Similarly, a good grip on algorithm theories to distinguish a few models such as support vector machines (SVMs) are of the essence. In addition, the know-how to partial differential equations, convex optimisation, quadratic programming, gradient descent, LaGrange, and so on will also prove useful for being an ML Engineer.
3. Distributed Computing and Data Modelling
As an ML Engineer, you are going to cope with larger datasets, and hence, chunks of such datasets must be evenly distributed among different machines so that data processing works smoothly. Apache Hadoop (a software utility) and Amazon’s EC2 (a cloud-computing service) are one of the best and cost-effective tools for distributed computing. Go get some training in these tools to increase your chances for the job.
Data modelling is one of the key requirements because an ML Engineer collaborates with business stakeholders to understand and analyse data requirements that can speed up business processes in conjunction with the available, in-house technology.
4. Creativity and Self-training
These underrated skills will lay the foundations for your curiosity and interest. If you demonstrate creativity and an aptitude for self-training, then business stakeholders are going to prioritise your job application because they surely need innovative ideas that can make their businesses stand out.
5. Passion and Analytical Skills
Last but not least, every ML Engineer needs to have these skills. Without passion, any ML Engineer would feel dragged at some point, and without analytical skills, one cannot be a competent ML Engineer because he/she is bound to analyse data requirements on a daily basis.