How to Become a Machine Learning Engineer?
Machine Learning Engineer is the #1 Best Job in the United States as per the popular employment website, Indeed.com. The job has a rate of growth of 344%. The demand for Machine Learning Engineers is increasing worldwide with every passing day.
Google Trends – Machine Learning Engineer
In simple terms, a Machine Learning Engineer is the resource building, researching, and managing the ML algorithms and Artificial Intelligence (AI) codes for the ML/AI platforms. ML Engineer is a crucial part of the Data Science team responsible for many other duties and tasks. The road to becoming a Machine Learning Engineer requires a correct combination of qualifications, skills, knowledge, and experience.
Educational Qualifications
A majority of the employers prefer the candidates with at least a Master’s degree in any of the subjects as Computer Science, Information Technology, Mathematics, or Statistics for the role of ML Engineer. Organizations can also select a candidate with a graduate degree and extensive experience in data modeling/data architecture/data management. Education background in programming and/or statistics is also on a high-priority list for the recruiters and the Talent Acquisition Managers.
Skills and Knowledge Needed to Become a Machine Learning Engineer
It is significant to have essential skills to be a good fit for the role of a Machine Learning Engineer.
Technical Skills and Knowledge Areas
Probability and Statistics: Many of the Machine Learning techniques and models, such as Bayes nets and Markov models, involve the application of probability. Statistical and distributions are also significant in model building and validation processes.
Advanced Programming: Programming is one of the core skills for a Machine Learning Engineer. The candidate must have familiarity with practical knowledge of the concepts, such as data structures, data algorithms, and likewise. There are different programming languages now available, and proficiency in at least one of these languages, such as Python, R, Ruby, or others, is essential for the role. Apart from programming languages, skills in database languages, such as SQL and Big Data platforms, such as Hadoop or Spark, can provide an edge among the rest of the candidates.
Applied Mathematics: Mathematics has numerous uses and applications in Machine Learning. It plays a relevant role right from the beginning. Such as the selection of an adequate ML algorithm for the datasets. Skills and knowledge in the applied maths concepts like linear algebra, multivariate calculus, distributions, integrations, etc., are essential for the Machine Learning Engineer.
Data Modelling and Evaluation: Data is the most important element in any of the ML projects and applications. It is expected of the candidate to have skills in data modeling and visualizations. For instance, the selection of the ML technique, such as classification, linear regression, or others, depends upon the data. One must be able to evaluate and determine the structure of the data set and identify the patterns hidden. Prediction of data properties, selection of error measures, and evaluation are the other data modeling and evaluation concepts essential for the role of an ML Engineer.
ML Algorithms and Libraries: The incorrect selection of the ML algorithm or library can cause adverse implications on the entire ML lifecycle. Sound knowledge of ML algorithms across all the three categories, viz. supervised, unsupervised, and reinforcement learning, is crucial for a Machine learning Engineer. The candidate shall also have the skills to determine the pros and cons of the ML techniques for specific applications.
Non-Technical Skills
Communication and Collaboration Skills: A Machine Learning Engineer has to interact with Business Analysts, Data Analysts, Developers, UI Experts, QA Engineers, and other resources. ML Engineer may also communicate directly with the client and the project stakeholders. It is essential for the resource to have effective communication and collaboration skills for seamless information exchange.
Problem Solving and Risk Management: It is certain to face numerous problems, risks, and issues during the lifecycle of an ML project. ML Engineer shall have the skills to understand and resolve these problems and manage the risks in an efficient manner.
Critical Thinking Skills: The Machine Learning Engineer shall be able to develop a logical connection between the problem, ideas, and solutions. Observational, analytical, and inference abilities can play an important role.
Practical Experience
It is not possible for a potential Machine Learning Engineer to develop all the above skills and knowledge from theoretical concepts. The Practical implementation and experience are of utmost significance to develop the essential skills and be eligible for the role of an ML Engineer.
Candidates shall work on a few ML projects to develop and acquire the skills. For example, Retail Price Recommendation is a beginner-level ML project using Python as the programming language. E-commerce is now extensively in use by customers and business owners. One can develop the application to automatically recommend adequate product prices to the customers. The project will provide an understanding of building and training a Random Forest model or a Neural Networks model.
Similarly, it will be good to work on loan default risk prediction applications using ML techniques. The application will assist the banks or other financial institutions in predicting the loan repayment capabilities of their clients. The project will aid in acquiring and developing skills in deep learning, neural networks, extensive feature engineering, and a lot more.
Machine Learning is an emerging technology with applications in almost all the industrial and business sectors. One can explore the real-world use cases to work on some of the ML projects to develop the essential skills.
Working on such projects will also present better insights into the types of risks and problems that appear during the ML projects. It will be useful in the development of technical and non-technical skills for the role of an ML Engineer.
The demand for Machine Learning Engineers is at an all-time high. It is essential to have the appropriate educational background along with the skills, knowledge, and experience for the role. One shall focus upon the collective development and improvement of technical as well as non-technical skills. Prior experience of working on some of the ML projects provides an edge among the rest of the candidates.
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