We’ve been talking about machine learning and artificial intelligence for a decade now. Machine learning engineer is a job title you’re likely hearing more and more these days with many online academies, universities, and boot camps offering- machine learning programs, master’s degrees, certifications. The diversity of machine learning programs demonstrates nothing but the growing demand for machine learning engineers. A Gartner report estimates that AI will create 2.3 million jobs by the end of 2020. And if you look at a similar report from the popular job portal Indeed, the job opportunities in the spectrum of AI have doubled over the last 3 years with the most in-demand job roles being Machine Learning Engineers, Deep Learning Engineers, and Data Scientists.
If you have been considering a new career in IT then there is one most-sought-after AI job role that should pique your interest : machine learning engineer. On the surface, it might seem like the best opportunity to leverage your mathematical and analytical mindset. But the more you think about the job role of a machine learning engineer, the more ambiguous it is. Yes, machine learning engineers deal a lot with math, numbers, and algorithms. But there’s much more to the machine learning engineer job role than that. Keep reading to find out whether a machine learning engineer job could be your future job title.
However, before we start defining the role of a machine learning engineer, let’s begin with understanding what is machine learning.
What is Machine Learning?
Machine learning is a subset of artificial intelligence that provides systems with the ability to learn without being explicitly programmed to facilitate data-driven decision making. It’s basically a data analysis method that identifies trends and patterns, learns from them to make accurate predictions without human intervention.
A simple and easy way to understand the concept of machine learning is the recommendation algorithms on Amazon, Netflix, Flipkart, and other consumer-facing services. Every time a customer searches for a product or a views a video, the machine learning algorithm behind the scenes adds more data points to its leaning. With increasing amounts of data, the accuracy of the recommendations to the user improves without any kind of human intervention.
A Day in the Life of a Machine Learning Engineer?
Let’s say there is an IT organization that runs a successful service based business to a wide audience online. The business requires continuous prototyping and mocking up of different website layouts. This is because the organization employs best UI/UX practices and learns continuously by running various A/B tests on their website. The website also records user behaviour through a tracking tool like HotJar that records scroll events on a webpage and mouse clicks. This helps the organization analyse user pain points by observing the behaviour on the website.
A machine learning engineers responsibility in this organization would consist of the following –
- Collate data from various HotJar users and run it against different machine learning algorithms to identify common issues and reasons of user confusion and pain points.
- Analyse the above gathered data to identify patterns in when, how, and why the user pain points occur.
- Build and deploy a machine learning algorithm that will capture images of whiteboard sketches of web layouts drawn by the UX team and produce well-finished website layouts that can be used by the Software Dev team. This can speed up the feedback loops involved in the improvement of website’s UX design.
- Build a machine learning model combining the A/B testing data and HotJar data along with Google Analytics data to suggest improved website layouts resulting in increased time spent on the website, higher customer acquisition rate, or any other profitable business goal.
- Deploying a model to predict the success rate of various website layouts recommended by the UX team.
As we can see from the above example, it is not so easy to define the exact role of a machine learning engineer because every industry and organization has their own specific requirements and implementations of automation practices to drive positive business outcomes. Machine learning engineers work on multiple diverse tasks , most of which are specific to the project. For instance, if a MLE is working on self-driving cars the key responsibilities of a machine learning engineer will include creating deep learning models, computer vision, programming, and sensor fusion.
Machine Learning Engineer Roles and Responsibilities
A machine learning engineer wears many hats right from data collection to data manipulation to model building to service deployment. Now that you’ve got a rough idea about what a machine learning engineer does, let’s get specific about the generic responsibilities and day to day tasks of a MLE –
- Machine learning is all about data and engineers spend a lot of time on getting familiar with data before beginning with the project to save time in the long run. The goal of an MLE is to become a subject matter expert of the dataset by identifying various features, outliers, and checking the distributions.
- Build machine learning models from scratch and help end-users understand the outcomes.
- Machine learning engineers also feed data into models developed by data scientists to scale them out to production level models which can handle terabytes of data for positive bottom-line outcomes.
- Responsible for automating infrastructure used by the data science team.
- Automate business processes by developing minimum viable products based on machine learning.
- Research and implement best practices to enhance and improve existing ML infrastructure.
- Constantly involved in improving the performance of models by deciding which machine learning techniques work best in production environment.
- Other than these responsibilities there a number of different tasks that an ML engineer is involved in – data mining, pattern matching and recognition, object detection, image recognition, and other domain specific tasks.
Machine Learning Engineer Skills
Professionals with solid data skills can make a career shift to become Artificial Intelligence/ machine learning engineers by developing competencies on NLP, applied math and statistics, software programming, and working knowledge of other ML tools like TensorFlow, Keras, H2O, Spark MLib, Theano, and more. Here is a quick summary of the prerequisites for ML Engineers and their required skill set.
- Math, Probability and Statistics – A good grasp of statistics measures, distributions, and probability is required to understand ML models derived from Hidden Markov Models, Naïve Bayes, Gaussian Mixture Models , etc. Exceptional mathematical skills are required to perform complex computations involved when working with algorithms.
- Programming – A career as a machine learning engineer requires learning one or more of these programming languages – Python, R , Java, C# along with knowledge of working with distributed computing systems like Hadoop or Spark.
- Software Engineering and System Design Principles– A MLE should have in-depth understanding of agile practices , various software development methodologies, and the modern dev tools from IDE like IntelliJ and Eclipse to components of a continuous integration and deployment pipeline of DevOps.
- Extensive data modelling, data evaluation, data architectural and data visualization skills.
Other soft skills that a MLE should develop are – Analytical thinking, Business Acumen, Problem Solving, and Communication skills. Learning these skills and gaining the required hands-on experience for a machine learning job typically begins with a comprehensive AI and Machine Learning Program.
Are Machine Learning Jobs Right for You?
Organizations across diverse industries – from healthcare to travel are integrating AI and ML into their top data initiatives . The heightened interest from organization to invest in ML is a clear indicator of the growing demand for machine learning engineers for cutting-edge research. Of the 10 lakh registered companies in India , 75% have invested or plan to invest in data science and machine learning. With a whopping 344% growth in number of machine learning jobs and average machine learning engineer salary of $146,085, it can be considered as an avenue for rewarding perks and lucrative pay checks. The big picture is very good for machine learning engineers, both in terms of potential earnings and job opportunities, making it one of the safest bets for a long and prosperous career.
So, now you have a better answer to what a machine learning engineer does, however, your picture of a machine learning engineer job role might not yet be complete. The next question you might have is, “What can I do to develop machine learning skills?” Gaining the right qualifications, developing the skills and knowledge, and building a project portfolio are an integral part of the journey to becoming a machine learning engineer. The career path to become a machine learning engineer is challenging but not impossible.
If you’re sincerely interested in knowing what this career is like from within, it’s time to dig a little deeper, practically. At Springboard, we have put together a comprehensive machine learning course that will help you gain the knowledge and skills needed to become a professional machine learning engineer. At the end of the course, not only will you have the skill set to apply for ML Engineer jobs but also have a project portfolio to show to potential employers. Check out our career path training for AI and machine learning with job guarantee.