The use of Machine Learning has continued to grow over the past few years, leading to more artificial intelligence applications and a rise in the number of engineering jobs available within the industry.
Machine Learning engineers are responsible for creating and maintaining algorithms capable of performing certain tasks. For example, Amazon uses Machine Learning to determine what other products you may like to buy, while YouTube uses Machine Learning for video recommendations.
Machine Learning engineers are in high demand worldwide and the high salaries these engineers can earn make the field an attractive endeavour.
But how do you become a Machine Learning engineer? That’s the question we’re going to answer in this guide. Here, you’ll find everything you need to know to decide whether a career in Machine Learning is for you. You’ll also find helpful resources on Machine Learning engineer salaries and training programs.
A Machine Learning engineer is a type of software engineer who processes data and uses it to train computers to perform a certain task. Machine Learning engineers use large datasets and connect them with a program that is capable of learning from the information before creating valuable insights.
For example, Machine Learning can be used to help detect credit card fraud or to provide a user with recommendations about what to buy next.
There are a few careers you can pursue in the Machine Learning engineering field:
Machine Learning Engineer: Machine Learning engineers create programs to ingest large amounts of information and learn from that data. They use big data tools to gather data, then train a program to learn from that information and perform a certain task.
Data Scientist: Machine Learning engineers are a type of data scientist. Data scientists are engineers who gather and process data to solve a particular problem and use mathematics to generate insights from a dataset.
Artificial Intelligence Engineer: Artificial Intelligence engineers use data science and Machine Learning techniques to train machines to conduct certain tasks and often work on specialist projects such as self-driving cars and natural language processing.
No matter what type of Machine Learning engineer you want to be, Knoma can help you find the right coding bootcamp that matches your unique needs and interests.
Machine Learning engineers create Machine Learning models and retrain existing systems. They use techniques from data science to help derive insights from a dataset and teach a computer how to recognise patterns in data and learn from those patterns.
A Machine Learning engineer also ensures that all models are always functioning, ingesting information and learning from what they consume. They use big data tools and their own programs to help a computer learn to perform a task on its own. They also scale up existing algorithms and help a company use Machine Learning to achieve its business goals.
Machine Learning engineers also study business problems and then design systems to help solve those problems. They have to choose the right datasets from which to work, build an algorithm and then run tests and experiments to optimise their engine.
Experts have predicted continued growth in data collection, data mining and the need for qualified computer scientists to analyse the data, meaning the job outlook for Machine Learning engineers remains positive.
The Bureau of Labour Statistics does not track Machine Learning engineers in their own category. Rather, they include them as part of the computer and information research scientists classification.
According to the Bureau of Labor Statistics, jobs in computer and information science are expected to grow by 16 percent by 2028, which they describe as “much faster than average.”
Machine Learning engineers command impressive salaries. According to Glassdoor, salaries for Machine Learning engineers range from £31,000 to £87,000 and the national average salary for Machine Learning engineers is around £52,000.
However, there’s no way to know exactly how much you can expect to earn because Machine Learning engineer salaries depend on the company you work for and where you live.
Your salary will also depend on how much experience you have. Senior Machine Learning engineers earn toward the higher range of the spectrum. Keep in mind that salary doesn’t include stock options, benefits or other employee perks offered by some tech companies, which you should think about when you’re evaluating a job offer.
There are a few different paths you can take to become a Machine Learning engineer, but the most common routes fall into one of the following groups:
Each path has its own benefits and drawbacks. In the past, only college graduates and self-taught coders became professional data scientists. However, coding bootcamps have emerged as the top option over the past few years.
Coding bootcamps offer a real alternative to a college education. Instead of spending four years in a university and taking on debt, aspiring coders can spend less than a year in a bootcamp learning everything they need to know to pursue a specific career in tech.
Coding bootcamps, which usually last between three and nine months, are intensive training programs designed to prepare you for a specific job in tech. During a bootcamp, you’ll work on a series of immersive projects and build up a portfolio of your work. You’ll also receive the career support you need to transition into your dream job in tech.
In order to succeed on your Machine Learning career path, there are several skills you’ll need to learn. Let’s break them down into two categories: technical skills and soft interpersonal skills. We’ll start with the technical skills for Machine Learning engineers.
Below are the technical skills you’ll need to master in order to succeed as a Machine Learning engineer. These skills include programming languages, data science techniques and other tech abilities.
Statistics and Probability: You should be aware of various statistical analysis concepts such as distributions, median, variance and mean, and know how to use these concepts to learn from a set of data. You should also be aware of probability concepts like independence and conditional probability, which will come up in your work.
Data Modelling: Data modelling is at the core of Machine Learning. You should be able to model a dataset and find patterns within that dataset. You should also be able to evaluate the usefulness of a dataset and ensure that your algorithms will be functional based on available data.
Computer Science Fundamentals: Machine Learning engineering requires the use of a variety of fundamental computer science concepts. You should therefore understand topics such as queues, trees, arrays and stacks. You should also be aware of the main types of algorithms, such as sorts and searches, and understand the architecture behind a computer.
Python and R: Python and R are two of the most popular data science programming languages. You should be proficient in at least one or both of these languages and be able to use various libraries associated with them to analyse a dataset and implement a Machine Learning algorithm.
Software Engineering: As a Machine Learning engineer, you’ll need to know the basics of software engineering as well. You should be familiar with how different parts of software come together to create a final program. You should also be able to query a database, call an API, interact with a web service and overall, have a good understanding of how to interact with software and the web.
You’ll need to have more than just technical skills to succeed as a Machine Learning engineer—you’ll need to have a whole set of soft skills to thrive.
Problem Solving: Coding is all about solving problems, so you should be a great problem solver. You should be able to break a problem down into smaller components, figure out different ways to approach them and come up with an efficient solution.
Business Mindset: Your algorithms will be based on the needs of a business—you’ll receive a project specification and have to translate it into code. Thus, you should understand the goals of your client and get to know what they expect from their data analysis and Machine Learning teams.
Iteration and Adaptability: Machine Learning is about trial-and-error, just like any other programming job. You’ll need to iterate in order to create a working program, and you’ll need to update your programs as more data becomes available and as the needs of a business change. You should be comfortable adapting to new specifications and iterating on your existing work.
Here are the steps you’ll need to follow to become a Machine Learning engineer: