How to become a machine learning engineer in 2025
Machine learning has exploded in recent years and continues to grow rapidly across multiple industries. Fortune Business Insights (2022) reported that the machine learning industry is expected to grow from USD 21.7 billion in 2022 to USD 209.9 billion by the year 2029.
The report further suggests that the global impact of the recent pandemic caused a massive spike in growth and demand for machine learning services over the last two years. With that said, what exactly is machine learning and why is it gaining such immense popularity across the globe?
To answer these questions, we are going to:
Machine learning defined
Machine learning is often classified as a subset of artificial intelligence which enables software stacks to become somewhat aware in that it is able to make accurate predictions in outcomes based on the data it is trained with.
Many experts in the domain of machine learning would argue that it is used to solve highly complex issues that may often be unseen by the average software developer or data analyst (Carleo et al., 2019).
Generally speaking, there are often three major classes of learning problems when it comes to machine learning, this includes: reinforced learning, unsupervised learning and supervised learning. Reinforced learning, which is also referred to as reinforcement learning subscribes to the notion of the learning agent which learns through trial and error by interacting with its environment – in this case there is no predefined data for the agent to work from. Unsupervised learning on the other hand implies that the agent is trained with the use of unlabelled data where no guidance or assistance is offered. While supervised learning is the most lenient of the three where the agent is trained using a set of data which is often labelled (Arora, 2020).
What is a machine learning engineer?
As you may have guessed by the title, a machine learning engineer is an individual who is able to perform research and develop artificial intelligence systems that may be used to generate predictive models through the process of automation.
It is a highly skilled and technical role which involves the use of software development techniques and a good understanding of both descriptive and inferential statistics.
A machine learning engineer usually works in a team that consists of data scientists, analysts and data architects. They often work alongside various other individuals from various disciplines throughout an organisation depending on the type of predictive models that need to be built in order to generate valuable insight from data that is often gathered by the organisation Gillis (n.d).
The responsibilities of a machine learning engineer
As mentioned before, the primary objective of a machine learning engineer is to design, create and deploy machine learning models where it is often required. In most cases, these engineers are responsible for the following, although this can vary between different organisations:
- Researching and deploying machine learning models.
- Identifying valid data sets used to train the machine learning model.
- Classifying the variations in data distribution in order to optimise the performance of the model.
- Authenticating the quality of the data that serves as an input for the model.
- Processing and transforming data sets.
- Performing statistical analysis such as multiple linear regression tests.
- Using the realised results to improve the machine learning model.
- Optimising the model by retraining it through a number of iterations.
- Using client and organisation requirements to develop machine learning applications.
What skills are required to become a machine learning engineer?
It should not come as a surprise that given the technical responsibilities of a machine learning engineer, the skills required to master the role are fairly technical as well. If you scour the internet you will often find that machine learning and artificial intelligence is a blend of multiple fields which include mathematics, statistics, software development and engineering.
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Mathematics and statistics are key disciplines that one should be familiar with in order to make sense of algorithms and data analytics. Where mathematics and statistics are concerned, some of the key concepts that apply to the domain of machine learning consist of calculus, linear algebra, probability and Bayesian statistics.
These skills are frequently associated with degrees in mathematics and statistics. On the other hand, software development and engineering are also overlapping fields in machine learning given that the role requires you to work with software in order to build models and applications. Programming skills are thus important especially when working with machine learning libraries to expedite the software development process.
Many machine learning engineers, statisticians and data scientists favour data driven programming languages such as Python and R. Gupta (2021) suggests that Python is by far the most popular programming language in the domain of machine learning given its world-wide adoption and ease of use as a data driven programming language.
The degrees associated with engineering and programming are degrees in computer science and information systems. Although they vary quite significantly, both disciplines deal with computing on a programmatic level while focusing on the need to generate data for visualisation and valuable insights.
