How to Become a Machine Learning Engineer

It seems like these days everyone’s talking about Artificial Intelligence and Machine Learning. Often times, these conversations are coupled with panic-stricken clickbait news stories dealing with robots taking human jobs, or other such alarmist nonsense.

But what is NOT nonsense is the fact that AI and machine learning are becoming increasingly common in all sectors of society. At the risk of sounding cliché, they are the wave of the future. And no, they don’t herald an age of mass unemployment; rather, they open the door to more potential careers and jobs.

With that in mind, it makes sense to consider a career in AI or machine learning, doesn’t it? For now, though, we will focus solely on machine learning, despite the fact that the two terms are so interconnected. You’ll see why later.

Preparing for a career as a Machine Learning Engineer? Take up the Machine Learning Training Course and learn to develop Machine Learning algorithms! 

So then, what’s involved in becoming a machine learning? This article will tackle this by defining machine learning, explaining what a machine learning engineer is and what they do, what the career entails, and how to become one.

Read on, and open yourself up to the possibility of a brand new career that’s on the cutting edge of today’s technology.

Machine Learning Defined

Let’s begin with what machine learning actually is. If we’re going to be throwing around these terms, we should know what they mean.

Here is a good definition, taken from this article:

“Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.”

So machine learning is a subset of artificial intelligence, but artificial intelligence is not necessarily a subset of machine learning.

What’s a Machine Learning Engineer?

A machine learning engineer helps software applications by leveraging big data tools to make sure those apps get the needed information to grow. Whereas data science is the description, prediction, and casual inference derived from structured and unstructured data. It’s used to help organizations make better decisions since informed decisions have a better chance of success. Both professions are relatively new, hence there are many instances of overlap. But without a doubt, the fields complement each other.

The Roles and Responsibilities of a Machine Learning Engineer

Now that we know what one is, what exactly does a machine learning engineer do? As mentioned earlier, machine learning engineers work with big data, specifically, they feed data into models, the latter which have been designed by data scientists (see where the overlap can occur?).

Additionally, machine learning engineers are responsible for taking theoretical data science models and scaling them out to production-level models, so that they can handle the resulting terabytes of real-time data. They also build programs for controlling robots and computers, of course.

Ultimately, machine learning engineer develops algorithms that enable a machine to look at its own programming data and identify patterns in it, thereby teaching itself how to understand commands and eventually think for itself. This is how learning is achieved.

For those who like lists, the responsibilities of a machine learning engineer are:

  • Understanding and using computer science fundamentals, including data structures, algorithms, computability and complexity, and computer architecture
  • Using mathematical skills for performing computations and working with the algorithms involved in this kind of programming
  • Producing project outcomes and isolating issues that require resolution, with the goal of making the programs more effective
  • Collaborating with the above-mentioned data engineers in order to build data and model pipelines
  • Managing the infrastructure and data pipelines that are necessary for bringing code to production
  • Demonstrating end-to-end understanding of applications that are being created
  • Building algorithms based on statistical modeling procedures, and building and maintaining scalable machine learning solutions in production
  • Using data modeling and evaluation strategy in order to find patterns and predict unseen instances
  • Applying machine learning algorithms and libraries
  • Taking a lead on software engineering and software design
  • Communicating and explaining complex processes to laymen
  • Keeping in contact with stakeholders for the purposes of analyzing business problems, clarifying requirements, and then defining the needed resolution scope
  • Analyzing large, complex datasets for the purpose of extracting insights, as well as deciding on the appropriate techniques
  • Researching and implementing best practices to improve the existing machine learning infrastructure
  • Providing support to engineers and product managers in implementing machine learning in the company’s products

It should also be pointed out that there are several categories of machine learning engineer. There’s the software engineer, who specializes in computer science fundamentals and programming, and software engineering and system design; the applied machine learning engineer, who focuses on computer science fundamentals and programming, which covers the application of machine learning algorithms and libraries; and finally the core machine learning engineer, who masters computer science fundamentals and programming, and is responsible for the application of machine learning algorithms and libraries, data modeling, and evaluation.

Why Should I Become a Machine Learning Engineer?

If that sounds like a lot of work, that’s because, well, it is. But no worries; with many responsibilities come many advantages and benefits.

For starters, let’s talk about job security. There are 9.8 times more machine learning engineers working today than five years ago, with 78,000 jobs in the machine learning field expected to be created by 2020. With these stats, it should come as no surprise that ML patents grew at a rate of 34% CAGR between 2013 and 2017.

Also, the field is still new, so machine learning job descriptions and requirements are still a work in progress, where most of the machine learning jobs are skill-based, as opposed to relying on a prerequisite such as needing a degree from a university. More on that skill acquisition later!

In other words, getting into machine learning at this stage in the game is like getting in on the ground floor, free of restrictive policies and procedures of questionable use and value. In some ways, it can be argued that the machine learning engineers of today can help set precedents for their successors years down the road.

As far as pay goes, according to Indeed, the average machine learning engineer salary in the US comes in at $145,000 per year. Even an entry-level machine learning engineer can command a $107,000 annual salary, according to payscale!

Do you have the right skills to begin your career as a Machine Learning Engineer? Try answering these Machine Learning MCQs and find out today!

The Path to Machine Learning Success

So how do you become a machine learning engineer, or upskill yourself in your present job to possibly include machine learning at some later date?

Here are the best steps to take to achieve machine learning engineer mastery!

  1. First of all, you should already be a software engineer, or at the very least have the concepts and skills for that position already locked down. Let’s be realistic here; you can’t simply walk into the world of machine learning engineers without some kind of computer background. Software engineer is the way to go.
  2. Acquire the necessary skills for machine learning, which are:

    a) Software engineering and system design. There’s the software engineering aspect coming into play again. Machine engineers need to understand how all of the parts work together and communicate with each other, as well as building interfaces for your piece that others can use. System design and software engineering best practices cover these requirements (which includes requirements analysis, system design, modularity, version control, testing, and documentation).

    b) Computer science fundamentals and programming. This covers data structures (e.g. stacks, queues, multi-dimensional arrays, trees, graphs), computability and complexity (e.g. P vs. NP, NP-complete problems, big-O notation, approximate algorithms), algorithms (e.g. searching, sorting, optimization, dynamic programming), and finally, computer architecture (e.g. memory, cache, bandwidth, deadlocks, distributed processing).

    c) Probability and statistics. Machine learning engineers need to have a grasp of the formal characterization of probability, including conditional probability, Bayes' rule, likelihood, independence, as well as the techniques derived from it (e.g. Bayes Nets, Markov Decision Processes, Hidden Markov Models). Engineers also require a good grasp of statistics measures, distributions, and analysis methods.

    d) Data modeling and evaluation. Machine learning engineers also need to find patterns in data, predict properties of instances which were unseen before and determine the right accuracy or error measure.

    e) Applying machine learning algorithms and libraries. Finally, machine learning engineers need to understand the standard implementations of machine learning algorithms. These can be accessed through libraries, packages, and APIs. Engineers need to know how to select the right model as well as choosing a learning procedure to fit the data. To round this out, engineers need to understand how hyper-parameters affect learning.

    f) Good communication skills. An effective machine learning engineer will find him or herself working either on a team or with teams from other departments. Since machine learning depends heavily on artificial intelligence, a good ML engineer will work well with those particular experts.

  3. You need to learn programming languages. An IBM report ranked Python, Java, and R as the top languages for machine learning engineers followed in turn by C++, C, JavaScript, Scala, and Julia.
  4. Get some practical experience. If your company/organization has a machine learning group already in place, take on some small projects and get acclimated to the field. Nothing beats hands-on experience!
  5. Read up on machine learning. There are numerous articles, videos, and podcasts out there that cover machine learning and can help sharpen your skills. If you want an easy start, check out this article on machine learning interview questions and this one on what skills you need to master machine and deep learning.
  6. Take a certification course. We’re saving the best (and most important) for last. Nothing beats getting machine learning certification by taking the right courses from a reputable educational organization. And speaking of which…

How to Get That Important Certification

Simplilearn offers you valuable opportunities to get machine learning certified. By taking the Machine Learning Certification Course coupled with the Artificial Intelligence Engineer Course, you will be equipped to either launch yourself into a new career in machine learning, or upskill your current skillset, thereby increasing your marketability in future career endeavors. This machine learning course comes with a variety of techniques to make you an expert in machine learning. By combining supervised and unsupervised learning, coupled with hands-on modeling and mathematical and heuristic aspects, this course will help you master machine learning concepts and prepare you for the role of a Machine Learning Engineer. The course gives you 36 hours of instructor-led training and four real-life industry projects with integrated labs. You will gain valuable expertise courtesy of over two dozen hands-on exercises.

This artificial intelligence course makes a great compliment to the machine learning course. You will not only master AI concepts but also master TensorFlow and the aforementioned Machine Learning. The course will also provide you with the programming languages which are required to design deep learning algorithms, intelligent agents and advanced artificial neural networks. Check out Simplilearn’s offering today, and get yourself started on a new career in a rapidly-growing field!

About the Author

John TerraJohn Terra

John Terra lives in Nashua, New Hampshire and has been writing freelance since 1986. Besides his volume of work in the gaming industry, he has written articles for Inc.Magazine and Computer Shopper, as well as software reviews for ZDNet. More recently, he has done extensive work as a professional blogger. His hobbies include running, gaming, and consuming craft beers. His refrigerator is Wi-Fi compliant.

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