For data analysts exploring new opportunities and seeking ways to move up the career ladder, one option is to become a machine learning engineer. A role high in demand and short in supply, machine learning engineers are vital not just to the data science industry, but to any organization that places data at the heart of its strategy.
But what exactly does a machine learning engineer do, and which kinds of skills are best suited to this position? In this article, we’ll explore everything you need to know about machine learning engineering. We’ll also explore some starting steps for those interested in pursuing a career in this field.
We’ll cover the following topics:
- What is machine learning?
- What do machine learning engineers do?
- Is there demand for machine learning engineers?
- How to become a machine learning engineer
- Next steps
Ready to expand your knowledge of machine learning engineering? Let’s kick off with the basics.
1. What is machine learning?
Machine learning is a branch of artificial intelligence (AI) that enables computers to learn from data without being explicitly programmed to do so.
Using algorithms, machine learning involves detecting patterns in data, allowing computers to make predictions—and, in many cases, decisions—without human intervention. Machine learning tools essentially allow computers to ‘think’ and ‘learn’ autonomously.
Machine learning was initially conceived in the 1940s, with the first executable algorithms developed throughout the 1950s and 60s. However, only with advances in technology and computer processing power has it entered its heyday. While the first machine learning algorithms were developed for the sciences, it is now an integral part of many industries, from healthcare to retail. It is used to automate complex tasks, provide insights, and drive better decision-making.
Contemporary examples of machine learning in action include:
- Automating customer service tasks, such as responding to inquiries or providing personalized recommendations
- Offering hyper-personalized marketing based on consumer interests and past behaviors
- Optimizing and managing supply chains by predicting customer demand and ensuring stock availability
- Improving medical diagnoses by analyzing medical images to diagnose diseases more quickly and accurately than using manual methods alone
- Supporting self-driving cars by using algorithms that detect objects in the environment and make navigation decisions
- Utilizing algorithms for facial recognition to improve security measures
The list, as you can imagine, goes on!
2. What do machine learning engineers do?
Machine learning engineers are responsible for developing and refining the algorithms utilized by machine learning tools. As a high-level role, it is their job to work with fellow data scientists and professional stakeholders to devise solutions to various problems. Typically machine learning applications might include:
- Natural language processing (for identifying customer sentiments, for example)
- Image recognition (such as that commonly used in policing or security)
- Machine vision (a subset of image recognition that allows computers to extract information from visual images)
- Speech recognition (for example, personal voice assistants)
- Financial modeling (for predicting stock prices or forecasting economic trends)
- Biomedical applications (such as discovering new drugs)
- Fraud detection (through monitoring of debit or credit card transactions)
- Recommendation engines (such as those used by Netflix or Amazon)
Once again, the list could go on!
The main thing to understand is that engineers and analysts use machine learning to automate tasks that are highly complex, time-consuming, and difficult for humans to complete accurately on their own.
However, while these are the clear benefits of machine learning, the trade-off is that ML algorithms need to be custom-designed and developed to meet a particular demand. This differs from most traditional data analytics algorithms, which tend to be more general-purpose and require—if not zero fine-tuning—then much less additional input.
What skills do machine learning engineers need?
There are some particular traits that all machine learning engineers require. On top of strong meta-skills (such as team working, problem-solving, resilience, and leadership) common technical skills include:
- Knowledge of the fundamentals of AI, data mining, and data analysis
- Knowledge of database systems, data warehouses, and other data tools
- Understanding of supervised, unsupervised, and deep learning
- Expert grasp of math and statistics
- Extensive knowledge of programming languages like Python, R, and Java
- Ability to debug and optimize machine learning models and create/manage machine learning pipelines
- Ability to create software, APIs and other interfaces that interact with machine learning models
- Understanding of cloud computing and distributed systems, such as Hadoop, Spark, or Flink
To demonstrate these skills, machine learning engineers typically have a high-level qualification such as a Masters or Ph.D. in a field relevant to their area of expertise.
What are a machine learning engineer’s typical responsibilities?
While a machine learning engineer’s responsibilities vary depending on the organization and specifics of their role, common ones include:
- Researching, designing, developing, and testing new machine-learning approaches
- Developing software to automate big data analysis
- Troubleshooting issues that crop up with new or existing algorithms
- Deploying machine learning models (i.e. moving from training data to outputs in a real-world setting, using real-world data)
- Interpreting and analyzing results to evaluate and improve a model’s performance
- Writing code to integrate machine learning models into other applications, such as websites, mobile apps, or industry tools
- Collaborating with other teams to ensure that machine learning models meet business needs
- Preparing presentations and reports on existing projects
- Contributing to the development of industry standards and guidelines for machine learning (which is much-needed in this relatively new and fast-evolving field)
Machine learning engineer vs data analyst: what’s the difference?
You’ve probably noticed that many of the technical skills and responsibilities outlined are similar to those of a typical data analyst. So what’s the difference between the roles?
The primary difference between a data analyst and a machine learning engineer is that the latter is a more senior role, requiring much broader expertise and usually taking a higher-level view. For instance, while data analysts may have a detailed and nuanced understanding of specific data sets and analytical techniques, machine learning engineers are typically more concerned with the skills related to how those data are processed.
Machine learning engineers also require more technical skills. Designing, developing, and deploying algorithms that make autonomous decisions brings new levels of responsibility. Machine learning engineers, therefore, need a deep understanding of data science techniques and software engineering best practices.
Finally, a machine learning engineer’s responsibilities go beyond seniority and technical skills. They also need an excellent grounding in areas like ethics and global citizenship. This is because, since even relatively small teams of engineers can have oversized impacts on large populations, understanding how to manage the power that comes with this role is as important as having the right technical skills.
3. Is there demand for machine learning engineers?
We’ve already driven home the specialist skills that a machine learning engineer needs. But is there any real demand for this complex role?
The short answer is yes.
The use of data is becoming more widely adopted in the business world. Not surprisingly, it follows that the demand for machine learning engineers is on the rise. And this trend is set to continue. Don’t take our word for it, though. According to the jobs site, Indeed, in 2019, machine learning engineer was the number one top role posted on their US website.
While Indeed’s top jobs since then have made space for other vital roles (such as social care roles that have hit the spotlight since the pandemic), software engineering and machine learning roles are still in the top ten. As artificial intelligence booms and more organizations adopt big-data-driven approaches, machine learning engineers will be one of the steadier staples of our fast-changing digital economy.
We can see this in how much machine learning engineers can earn. To get an approximate idea, check out this guide to learn how much you can earn as a big data engineer.
4. How to become a machine learning engineer
If you’re a data analyst and are keen to extend yourself or climb the career ladder, becoming a machine learning engineer is certainly an achievable goal. First, though, you’ll need to expand your skills in software engineering and artificial intelligence.
Here are our top tips for becoming a machine learning engineer.
1. Keep your data analytics skills up to date
As a machine learning engineer, you’ll benefit greatly from a solid background in data analytics. Take the time to brush up or perfect your knowledge of data analytics skills such as programming languages, statistical techniques, data warehousing, and data visualization. If you’re already a data analyst, why not identify opportunities to do this in your current role? Alternatively, explore these topics in your own time.
Take action: Check out these 9 ideas for expanding your project portfolio.
2. Learn more about artificial intelligence
While a cursory grasp of the principles of artificial intelligence is sufficient for data analytics, you’ll need more than that to become a machine learning engineer. As a core subset of AI, machine learning engineering means having a thorough understanding of the intricacies of AI, such as supervised and unsupervised learning algorithms, neural networks, natural language processing, computer vision, and more. Take the time to explore these topics. Start at a high level and then dig deeper into the software side as your skills improve.
Take action: Brush up on the basics. Some great AI blogs to get you started include the DeepMind Blog, AI Time Journal, and The Gradient.
3. Gain experience with software engineering
While software engineering skills are beneficial for data analysis, they’re not always necessary. For a machine learning engineer, though, it’s an integral part of the role. Even if you’re already familiar with programming languages like Python, you’ll need to up your coding game. Consider picking up a new language, such as Java or R, and get some practice in debugging and optimizing machine learning models. A great way to improve your software engineering skills is by getting involved in an open-source project. Doing so can help you learn as part of a community of practice.
Take action: Brush up on the basics of a new programming language or look for machine learning projects on GitHub.
Many of the best opportunities for machine learning engineers come through personal connections. Link with other engineers on social media or attend conferences, such as ICML, NeurIPS, and CVPR. You might also want to join professional organizations like the Association for Computing Machinery (ACM) or the Institute of Electrical and Electronics Engineers (IEEE). At the very least, check out their jobs or news pages.
Additionally, it’s worth joining online communities like Kaggle and Reddit to get advice from experienced professionals and to stay up-to-date on the latest developments in the field.
Take action: Check out Meetup and Eventbrite for local events, or join established LinkedIn groups to connect with others interested in machine learning.
6. Get certified
There are many certifications available in the field of machine learning engineering. Consider taking one or two courses to demonstrate your expertise. If you’re not sure yet, you can start out by looking for a free short course. However, having a respected machine learning certification can be a valuable addition to your resume. Websites like Coursera and Udacity offer a range of free online programs for machine learning engineers, so why not check those out for starters? If you like what you see, you can always enroll in a more comprehensive paid course.
Take action: Check out some online courses, or explore machine learning engineering webinars to learn more about the topic first.
Use these basic steps as a framework, and you’ll soon work up the skills, knowledge, and connections you need to break into this fascinating field. Remember: machine learning is still in its infancy. That means the possibilities for growth are yet to be defined, and that’s a pretty exciting prospect. The world is your oyster!
5. Next steps
So there we have it, a complete introduction to machine learning engineering.
If you’re an experienced data analyst looking to switch to machine learning engineering, there’s never been a better time to do it. As a core aspect of many companies’ strategies, the demand for this all-important role is higher than ever. Furthermore, in an economy where jobs are set to change, evolve, and disappear year by year, machine learning engineering is one rare position that’s very likely here to stay.
Of course, in any job, ensuring you have the right skills is vital. Take the time to brush up on your data science, artificial intelligence, and software engineering know-how, and practice as much as possible. And don’t forget to network with other professionals in the field, too.
If you want to learn more about a career as a machine learning engineer, sign up for this free, 5-day data analytics short course, or read the following articles: