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How to Become a Machine Learning Engineer in 2022

Posted in Machine Learning
How to Become a Machine Learning Engineer

By now, you have probably heard of AI (artificial intelligence) and ML (machine learning). If you are in the tech industry or follow it closely, you’ve probably noticed that it’s almost impossible to escape either one of them. Machine learning and artificial intelligence seem to be the future — more and more companies across more and more industries are starting to adopt the technologies to bring about efficiency and automation to their organizations.

Because of how widely adopted ML and AI are starting to become, the demand for machine learning engineers has simply continued to grow. If you’re here, you’re probably wondering how to become a machine learning engineer, and whether it’s truly a field that interests you.

In this complete guide on how to be a machine learning engineer, we’ve put together everything you need to know. Here, we tell you all about machine learning and what engineers do. Read on to find out more about how challenging it is to become an ML engineer, the requirements to be one, and what kind of jobs you can find in this field.

What is Machine Learning?

It can be difficult to understand what machine learning is if you are completely new to the subject. However, we can also simply say that machine learning is a subset of the technology we call artificial intelligence. Humans developed artificial intelligence, known also as AI, to mimic human behavior and work smartly without much human input. 

Machine learning is a technique through which we apply artificial intelligence to create systems capable of “analyzing” data and “learning” patterns. Machine learning is also capable of making predictions, classifications, decisions, or similar tasks on data — and it does this all without much human intervention. In machine learning, machines use various algorithms to learn from data and previous experiences. ML is not explicitly programmed.

In one sentence, we can then say that machine learning, as a subset of AI, focuses on the development of computer programs capable of accessing data and using that data to learn and improve — without being coded or programmed to do so.

If that’s still unclear, let’s take a look at an analogy: as a human, you experience something or make a mistake and generally learn from these past experiences. The lessons you’ve learned allow you to make better decisions in your future. Machine learning (ML) trains a computer to learn using historical data, allowing it to perform its tasks better in the future.

But how does that translate to real-world use?

Take the perfect example of online shopping. In online shopping, when you visit a website and use your account (or even just your browser with cookies and trackers) to purchase an item, your information becomes historical data for the ML model. The ML model learns from this data and now knows what you are interested in or need whenever you visit. 

And so, on your next visit to the website, you will then see similar products on display. Another example is if you buy an item in regular intervals, the ML model can see this pattern and recognize when it is about time for you to buy the product again. And so, the website may send you an email reminding you to purchase your item around those same intervals.

Future of Machine Learning

Machine learning has limitless use cases that can change the world and positively impact various fields like education, computer science, finance, and more. Currently, machine learning is already applied in critical areas like healthcare to reduce the risks for the patients, locate the issues in the business, and find loopholes in any process.

This technology will not go anywhere and will instead continue to grow in the future. Hence, the demand for machine learning engineers will increase exponentially.

Why Learn Machine Learning?

We can easily say without mincing words that machine learning and artificial intelligence in general, are technologies that are currently taking the world by storm. ML and AI are the future. With so many industries starting to adopt these technologies, it’s impossible to deny that one day, ML and AI will become one of the major driving forces of economic capacity and growth.

Don’t believe us? You don’t have to — just look at this MarketsandMarkets report that shows that the global machine learning industry was expected to grow by 7.78 billion from 2016 to 2022. The same report states that we should have expected the ML market to be around $8.81 billion in 2022, up from $1.03 billion in 2016.

Those figures alone are impressive, but if you look at this Fortune Business Insights report, it clearly states that in 2021, the global ML market was valued at a whopping $15.44 billion. That’s 6.63 billion over the expectation — and an entire year earlier to boot.

What this data shows is that machine learning is seeing exponential growth and will continue to do so as more industries adopt artificial intelligence into their practices. Needless to say, the demand for professionals knowledgeable in machine learning will continue to grow at a high rate. In S&P Global Market Intelligence’s 2021 article on AI and machine learning outlook, author Nick Patience states that their latest survey found that machine learning engineers are the second most sought-after role in AI.

Essentially, learning ML can lead you to a well-paying, highly in-demand job in various industries. Becoming a machine learning engineer can be a career pivot that leads you to great success.

What is a Machine Learning Engineer?

Machine learning engineers are often computer programmers. They design and build AI or self-running software, which then learns from data. As the software learns from data and experience, it can then begin to automate predictive models.

Some people who look at a machine learning engineer’s job description may think that these engineers are simply data scientists focused on building models. And while they’re not wrong, the truth is that building models comprise only a small percentage of a machine learning engineer’s overall job responsibilities.

ML engineers are somewhere in between data science and software engineering. Sure, they have to understand data structures and models. But beyond that, they also have to be able to deploy models they’ve built into software.

As more industries shift into the reliance on big data and automation, and as more organizations look for things to make their businesses run more efficiently, the demand for ML engineers will simply continue to grow. 

All of this may sound appealing, but before you even start thinking about how to get a job in machine learning, you should first know more about machine learning job requirements and responsibilities.

What Does a Machine Learning Engineer Do?

In simple terms, ML engineers teach systems and software to learn from data and experience on their own with little to no human input or intervention. 

You’ve probably come across the work of machine learning engineers at one point or another. If you’ve ever heard of the “YouTube algorithm,” that’s a solid example of a self-learning model. Other examples are your social media feeds, which over time start showing you things you most frequently interact with; and online shopping suggestions, which will show you items you’ve previously searched for or even items relating to something you’ve just bought.

To perform their actual jobs, machine learning engineers do a number of different tasks, such as —

  • Using machine learning libraries and programming languages to run machine learning experiments
  • Deploying their ML models into software
  • Optimizing their models for performance
  • Handling their models and ensuring scalability
  • Performing some data science work such as data analysis
  • Coming up with use cases
  • Doing data engineering work to keep databases and system backends flowing smoothly

Beyond this variety of tasks, ML engineers also collaborate with other colleagues such as researchers, data scientists, other software engineers, and even project or product managers.

A Machine Learning Engineer’s Roles and Responsibilities

Machine learning engineers generally work with big data by feeding data into ML models (often designed by data scientists, but they can also be designed by the ML engineers themselves). 

However, they have plenty of other roles and responsibilities, such as:

  • Taking theoretical models and scaling them so they can be production ready and capable of handling huge amounts of data in real-time
  • Building programs and software; taking lead on various software design and engineering responsibilities
  • Using strategies in data modeling and evaluation to find any present patterns which can then allow prediction
  • Studying, analyzing, and transforming data science prototypes
  • Designing new ML systems
  • Researching and implementing the right ML tools and algorithms
  • Selecting and using proper data representations and dataset methods
  • Performing statistical analyses, tests, experiments and fine-tuning using various test results
  • Extending the present ML frameworks and libraries
  • Supporting other stakeholders in the implementation and application of ML in the products

Machine learning engineers are also responsible for developing the algorithms necessary for enabling AI and software to learn from data and experience so it can “think” for itself and teach itself what to do and how to respond to commands.

Step-by-Step Guide: How to Become a Machine Learning Engineer

It can feel incredibly intimidating to even begin thinking about how to become a machine learning engineer. To help you out, we’ve put together a step-by-step guide. This guide will take you through the process in five actionable steps. See the steps below:

1. Learn to Code

If you are approaching a career in machine learning as a complete newbie, you may want to start by learning to code. 

One of the first languages you can pick up is Python, which is now one of the most popular programming languages. Python and its libraries are often used in various scientific, statistical, and artificial intelligence/machine learning applications, which is why it’s a good idea to familiarize yourself as you start. Learn Python and its libraries (a good one to start with is PyTorch).

You may also want to get comfortable with Github and SQL, which are tools that most ML engineers, software engineers, and IT professionals use.

Other languages you may want to consider learning include:

  • C++
  • R
  • SQL
  • Scala
  • Java
  • MATLAB

2. Join a Course

You can always learn machine learning on your own. There are plenty of self-taught professionals in the computer science field. However, it can be quite challenging to learn and find a job in the industry without at least joining some form of course, bootcamp, or program. 

There are a vast variety of courses available to help you learn each skill you need to become a qualified machine learning engineer. There are even bootcamps and programs designed to teach you everything you need from start to finish. Choose one that suits your needs and your budget and gives you the support and certification you seek.

It’s important to first learn the fundamentals of software engineering and then the fundamentals of data science as you’ll need a combination of both to succeed in ML engineering.

3. Start Building a Portfolio

One of the first things you should do once you finish your courses is to start working on projects to build your portfolio. Sometimes your courses will provide you with capstone projects you can add to your portfolio. In the absence of these, you can always try working on an ML project of your own.

You can even just start out by reviewing or recreating projects provided by online resources on the subject.

4. Join Some ML Communities

Learning can be challenging on your own, especially if you have no one to bounce ideas off of or ask for feedback when you need it most. Joining an online ML community can solve this problem. Consider a community like Kaggle, which is a great place for data scientists and ML professionals to gather. Kaggle even hosts some ML challenges that can help you build your skills. You can even earn some prize money in some of them.

5. Start Applying to Jobs

Once you’re confident enough in your skills and you’ve built up a portfolio you are satisfied with, the next step is to start applying to jobs! Expect to receive rejections at the beginning. There’s also no harm in starting with internships or entry-level positions to help you gain the experience you need in the field before you start working on finding a proper ML engineer career.

A Roadmap to Becoming a Machine Learning Engineer

How Long Does It Take to Become a Machine Learning Engineer?

If you thought a machine learning engineer career was easy, you might be disappointed — there’s a significant learning curve as you get started. However, the steepness of the learning curve you face will depend on whether you have had prior experience in data science, computer programming, and statistics.

Let us preface this by saying: you don’t need a machine learning engineer degree. Most of the time, you can take an ML course lasting six months and that could be enough to get you in the door and looking for jobs.

However, it can take you much longer if you are starting from scratch and need to learn programming and data science, which will both require additional time for courses and bootcamps. You can also expect to take longer if you are learning on your own, part-time, at your own pace.

Of course, learning is just half of the battle. Part of how to get a machine learning job is getting experience in the field. You can earn experience by:

  • Getting a solid knowledge of all aspects regarding machine learning technology
  • Getting practical knowledge through real projects using real data (Kaggle is a great resource for this)
  • Finding an internship or an entry-level position in machine learning projects

Requirements for Getting a Job in Machine Learning

If you’re trying to become a machine learning engineer, there are five crucial requirements you’ll need to meet. You don’t need a degree to earn these machine learning engineer requirements — in fact, there are many self-taught programmers, data scientists, and data professionals who have managed to break into the field. However, you will need to be able to demonstrate your understanding and expertise on the subject one way or another.

The five vital requirements to becoming an ML engineer are:

1. Certain Programming Languages

Knowing programming languages is vital if you are an aspiring machine learning engineer. You need to be able to comfortably work with certain programming languages if you want to be able to work on different types of data and software related to ML. 

Currently, there are a few different languages used in ML and AI applications, including but not limited to Python, C++, and Java. 

If you’re wondering how to become a machine learning engineer without a degree, start here. Although there are ways for you to learn machine learning without knowing how to code, it’s still a necessity to know these programming languages because the great majority of job opportunities in ML will require you to be very familiar with certain programming languages (though which languages in particular may vary depending on the company).

2. Specific Computer Programming Skills

Once you start looking for work as an ML engineer, chances are a vast majority of the job postings you come across will require you to have programming skills. Companies tend to look for employees who are experienced programmers because they must be able to deal with huge amounts of data. They also need to be able to scale and implement ML models to production level.

When you think of “computer programming,” you probably think of coding — and you’re not wrong. However, coding is just part of the job. There are other things companies may want you to be experienced or knowledgeable in, such as:

  • Working with data structures (stacks, graphs, multi-dimensional arrays, queues, trees)
  • Algorithms (optimization, sorting, dynamic programming, searching)
  • Computability and complexity (P vs. NP, big-O notation, NP-complete problems, approximate algorithms)
  • Computer architecture (bandwidth, memory, deadlocks, cache, distributed processing)

3. Data Modeling and Processing

You can’t be a machine learning engineer without being comfortable with data modeling and processing. It’s vital to have skills relating to this matter because as an ML engineer, you’ll be handling data that your ML models will learn from.

There are a few things that ML engineers are responsible for when it comes to data modeling and processing. These things include:

  • Finding patterns (correlations, clusters, and eigenvectors)
  • Predicting properties of last unseen instances (classification, anomaly detection, and regression)
  • Error measure process (log-loss for classification)
  • Evaluation strategy (training-testing split)

4. Probability and Statistics

A machine learning engineer can find critical problems in the data through statistics and probabilities because these methods help to extract the information in the best mathematical way possible.

A few things you may want to learn about include:

  • Formal characterization of probability (conditional probability, Bayes' rule, likelihood, independence)
  • Techniques derived from statistics (Bayes Nets, Markov Decision Processes, and Hidden Markov Models)
  • Statistics measures (mean-variance and median)
  • Distributions (uniform, binomial, Poisson and, normal)
  • Analysis methods (ANOVA, hypothesis testing)

5. System Design and Software Engineering

Machine learning engineers work on software that can fit in the broader ecosystem of a company’s services and products. It means they have to understand how the parts work and communicate using different methods. Machine learning engineers need to use the system design to have a complete machine learning ability for any particular task.

Machine Learning Engineer Qualifications

Machine learning qualifications are similar to the skills of domain experts, data scientists, software engineers, statisticians, and those with a significant amount of knowledge in advanced mathematics and statistical fundamentals. That includes an excellent understanding of algorithms.

Skills that are mentioned below are essential to step up in this field of machine learning and boost your career faster. You can consider learning the skills below through online courses and the like:

  • Collaboration, interpersonal skills and similar “soft skills” 
  • An understanding of algorithms and data structure techniques
  • Expert skills in Python, C, and C++ development
  • NLP or Natural Language Processing
  • Computer vision
  • Multimodal fusion
  • ASR or automatic speech recognition
  • Deep learning

What Types of Jobs Can You Get if You Learn Machine Learning?

If you’re wondering what types of jobs you can get if you start learning about artificial intelligence and machine learning, there are really quite a few. Of course, you can become a machine learning engineer. As of writing, Indeed.com states that the average base machine learning engineer salary in the United States is $123,755 per year.

However, if you want to work in machine learning but don’t necessarily want to take on the engineering, there are other careers you can consider. These careers often have overlapping requirements and qualifications, so you won’t really need to learn much more to start finding jobs in these fields.

Consider jobs such as:

  • Data Engineer - Avg. Base Salary $136,238 - Data engineers are often software engineers who are responsible for building systems for data. These systems then collect and manage data then convert it into usable information. A career in data engineering is great for those who love paying attention to detail and following guidelines in engineering. Data engineers are also in high demand, which often translates to job security and high salaries.
  • Data Scientist - Avg. Base Salary $141,109 - Data scientists are responsible for analyzing data to extract meaning from it. They interpret data using methods and tools taken from machine learning and statistics. Often, being human also helps data scientists put meaning to the data they handle. Some data scientists are also responsible for developing algorithms that can help data models forecast outcomes.
  • Software Engineer - Avg. Base Salary $126,552 - Software engineers are often the professionals responsible for designing and creating systems and applications for various uses. Generally, the solutions they create are used in solving real-world problems. Software engineers, who are also sometimes called software developers, make applications and programs for computers.

There are other emerging jobs that you can also consider, such as machine learning researcher and natural language programming (NLP) scientist.

If you’ve made up your mind that you want to become a machine learning engineer, there are a few online courses and bootcamps that can help you jumpstart that process. Here are a few of our recommendations:

  • Machine Learning Bootcamp: Deploy Algorithms and Build a Portfolio in 6 months by Springboard- Springboard offers its students a unique curriculum which they can learn on a flexible schedule. This course in particular is 100% online and also provides on-demand one-on-one mentorship. With this bootcamp, you can develop the necessary qualifications for becoming an ML engineer. You’ll also develop a machine/deep learning system and get practical experience in building a prototype and deploying it. Once you graduate, you’ll have a certificate and a portfolio.
  • AWS Machine Learning Engineer Nanodegree by Udacity - Udacity is well-known for providing high-quality courses and education in the form of “Nanodegrees.” This particular Nanodegree in Machine Learning teaches you all the job-ready machine learning and data science skills you need to pivot into your new choice career. Udacity’s certificates are generally recognized and accepted by many employers worldwide as many of the courses on the platform are co-developed with tech giants and authorities in the space.

If you’d rather start with books, we’ve put together a list of the:

20 Best Machine Learning Books for Beginner & Experts in 2022

Conclusion

Artificial intelligence and machine learning are here to stay. There’s no doubt that more companies will begin to adopt these technologies in the future, especially with how much potential both have at the moment. As more companies adopt AI and ML, the demand for machine learning engineers will only continue to grow.

We hope that this comprehensive guide on how to become a machine learning engineer has helped you figure out the steps you need to take. Once you’ve learned and polished the skills you need to start your career in the field, you can prepare yourself for job interviews with these machine-learning interview questions.

Frequently Asked Questions

What qualifications do I need to become a machine learning engineer?

To become a machine learning engineer, you must at the very least:

  • Know programming languages like Python and C++
  • Have programming skills
  • Know the fundamentals of data science and have knowledge in statistics and probability
  • Know software engineering and system design

How long does it take to be a machine learning engineer?

Courses and bootcamps to become a machine learning engineer can take up to six months. It may take you shorter or much longer, depending on the skills and knowledge you are starting out with.

Do you need a PhD to be a machine learning engineer?

Not at all. ML engineering is one of many fields in computer science that you can break into without formal education, though you may want to take a bootcamp or certification course at the very least.

Can a fresher get a job in machine learning?

Absolutely, if you have the relevant skills and a portfolio. Taking a bootcamp or course on ML can also help you significantly.

Are machine learning engineers in demand?

Yes! As more industries begin to adopt automation through artificial intelligence and machine learning, the need for ML engineers will only continue to grow — and exponentially so.

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Simran Kaur Arora

Simran Kaur Arora

Simran works at Hackr as a technical writer. The graduate in MS Computer Science from the well known CS hub, aka Silicon Valley, is also an editor of the website. She enjoys writing about any tech topic, including programming, algorithms, cloud, data science, and AI. Traveling, sketching, and gardening are the hobbies that interest her. View all posts by the Author

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