There’s a reason it’s called “big data”: in 2020, we’ve reached 40 zettabytes of data (in other words, 40 followed by 21 zeroes). That magnitude of data can’t be processed alone by all the big data professionals in the world. That’s why it’s become necessary to have the help of computer machinery in order to make sense of it all. Enter the age of Artificial Intelligence.
Machine Learning is the implementation of AI and algorithms in order to access, process, analyze, and generally seek to understand available data. Once machine learning systems are up and running, they operate more or less on their own, adjusting to the information they’re given as time goes on. But machine learning systems have to be put in place first. That’s where a Machine Learning Engineer comes in.
Machine Learning Engineers are part software engineers and part data scientists, utilizing their coding and programming skills to collect, process, and analyze data. It’s Machine Learning Engineers who create algorithms and predictive models utilizing machine learning to help organize data. These machine learning systems are used by every other big data job throughout the process.
When a bot is used by a particular business for chat purposes or data collection, that bot is created by Machine Learning Engineers. Any algorithms used to sort through relevant data are the work of Machine Learning Engineers. They even help to scale predictive models to best suit the amount of data relevant to the business. Some of the duties of a Machine Learning Engineer include:
Think about the algorithms used to sort the relevance of a search on Amazon or that predict the movies that a Netflix user might want to watch next. These, along with the search engines you use just about every day and the social media feeds that you check, are what they are due to the work of Machine Learning Engineers.
Many jobs within the big data industry get lumped in with that of Data Scientist. It’s understandable, as there’s quite a bit of overlap between the various data jobs. Data Scientists and Machine Learning Engineers do rely on each other in order to do their jobs well, but those jobs are distinct. Some of the duties that they have in common include:
From there, however, data scientists and Machine Learning Engineers are quite different careers.
A Data Scientist is the main position that asks questions when it comes to big data. Data Scientists take undefined sets of data and ask questions relevant to the business such as “Why did profits fall over the last quarter?” or “Who engages with this business the most?” They ask questions and then create data collection and analyzing processes in order to glean the answer from that data. They pass that along to data analysts, who determine the answer and create a report to show to the business executives.
Data Scientists create predictive models and work with algorithms in order to help answer the questions that they put forth with the data. However, the main focus of their job is the scientific analysis, not the tools that they use for it. It’s for this reason that data scientists rely on Machine Learning Engineers to do their jobs properly.
Machine Learning Engineers are part of the cog that helps to make sense of the data collected for the business’s purposes, but Machine Learning Engineers are more focused on creating the tools that will make that data easier to collect and classify. They develop the algorithms, AI, and other machine learning methods that data scientists, data analysts, and so on. Machine Learning Engineers also create the algorithms that determine the user experience on things like streaming sites, social media, or online marketplaces.
In this category, the line might be a bit more blurred. Both Machine Learning Engineers and data engineers work with data and both approach it from the same sort of background and framework. However, the job that they do is still different from one another. A Data Engineer is primarily focused on the acquisition of data. They create the frameworks in which data is retrieved and stored, and they’re constantly on the lookout for new ways to do so. These systems are constantly maintained by the data engineers for optimization. A Machine Learning Engineer works specifically with developing algorithms and machine learning processes, and letting the machines do the work from there. These systems must be maintained, of course, to make sure they’re running smoothly, but it wouldn’t be truly machine learning if the algorithms didn’t develop and change with the information received.
Financial stability is a chief concern with any career. After all, it’s through this career that you’ll be able to pay your rent or mortgage, insurance, and the rest of your livelihood. Fortunately, like all jobs in big data, Machine Learning Engineering positions are very financially stable and even lucrative. In fact, due to the high demand for Machine Learning Engineers in all different types of businesses, Indeed reported the average Machine Learning Engineer salary in 2019 to be $146,085, making it one of the higher paying technology jobs.
Using Indeed again as a source, Forbes called the job title of Machine Learning Engineer “the best job in the US” in 2019. Job security is also quite comfortable within this career. With the data that we use as a society constantly expanding, the need for Machine Learning Engineers is only growing. When you start at your Machine Learning Engineering job, you may find yourself there for as long as you like, with opportunities to grow within the position.
To be honest, you will probably not begin your career in data working as a Machine Learning Engineer. In addition to the right training, you will need a bit of experience before you can access this role.
Some of the undergraduate degrees you might pursue if you’re interested in a career as a Machine Learning Engineer include Computer programming, Computer science, Data science or Mathematics.
There are several ways you can acquire the skills that are necessary to work in Machine Learning. You can either go for a Master’s in Computer Science or Software Engineering, or you can join an intensive data science bootcamp. You can also combine data science and web development bootcamps to acquire skills in both domains.
As stated above, the career of a Machine Learning Engineer is one that you grow into, not an entry level position. However, you can start in a variety of other data or computer engineering positions in order to move up to the Machine Learning Engineering position. If you come from more of a data background, data scientist, data engineer, or data analyst might be the right option. If you’re more attracted to the computer element, start your career as a computer engineer, software engineer, or software developer. All of these will help give you the experience you need to eventually land your dream job as a Machine Learning Engineer.
More than education and experience, to become a Machine Learning Engineer, you’ll need to have the right set of skills. These skills can be learned and likely picked up in your education and work experience, but knowing what they are will help you to hone them so that you can stand out in the job search. Machine Learning Engineering skills include:
Do you know programming languages such as R and Python? Do you understand different types of data structures, like stacks, queues, trees, graphs, and multi-dimensional arrays? If you know computer architecture like the back of your hand and you’ve already had some experience programming algorithms, you’re beginning to have the skills needed to become a Machine Learning Engineer.
Data science is all about finding patterns within the data that can inform behavior and engagement of the users generating the data. Data modeling creates the structures and processes that help to find those patterns, including clusters, correlations, and anomaly detection. A Machine Learning Engineer should be familiar with all different forms of data modeling and how to constantly evaluate and analyze them to make sense of them.
What kind of ML technology should be implemented for a particular project? What algorithms should you use, modeled after what past successes? Can you scale that ML technology to fit the needs of the business? This is the kind of thing that really makes a Machine Learning Engineer stand out from the crowd.
Machine learning tackles the elements of life — and data, particularly — of which we can’t be certain by utilizing patterns in data as a way of making predictions and allowing the algorithms to perform accordingly. This ability relies heavily on skills like statistics and probability. Not only should you have a good head for statistics and probability naturally and familiarity with different methods of probability, but you should be experienced with measures, distributions, and different methods of analyzing those statistics.
Nervous about your upcoming interview for the Machine Learning Engineering job? Most of the questions will be used to test your knowledge of machine learning and data. With the right work experience and education, you should be able to ace these questions. Remember to approach your interview with composure and confidence. Here are some of the questions you might receive in your interview:
Want to hone your skills as a Machine Learning Engineer? Want a competitive edge when you set out for your dream job? Le Wagon offers a course in Data Science that can help you reach your objectives! In this bootcamp, you can learn from experts in the field and network with others in big data. You can spend 2 weeks covering machine learning, as well as deep learning and a chance to work on a full project. The goal of our bootcamp is to furnish you with both the skills and the community you need to thrive in your field. Combined with a successful experience in Web Development and a strong motivation, our Data Science bootcamp will definitely allow you to succeed in a Machine Learning Engineer position.
Download our syllabus below to discover our Data Science bootcamp and learn more about our alumni and community ! And for answers to frequently asked questions, head here.
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