7-Step Guide to Become a Machine Learning Engineer in 2021

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ProjectPro

13 min readFeb 18, 2021

Spoiler Alert: Becoming a machine learning engineer can sound like a hard-to-reach goal but let us tell you the truth — it isn’t as hard as it seems. And yes, we’re talking to you — the person who’s reading this because they’re wondering what is a machine learning engineer, what does a machine learning engineer does, how to become a machine learning engineer, and, more importantly, whether they can pull it off.

So you are considering learning machine learning skills, and you’ve heard that becoming a machine learning engineer is the way to go. That’s very savvy because machine learning engineers are the Swiss Army Knife of the data world. Having that designation means you can build end-to-end machine learning solutions, which is a highly marketable skill set considering the fact that it has been the fastest-growing job title in the world since 2019. But what does it actually take to achieve the designation of a machine learning engineer?

Becoming a machine learning engineer looks like a daunting task because one needs to possess a versatile skill set with knowledge of more than one programming language. A true machine learning engineer is a unicorn. That is why many machine learning tools are available to make it easier. One cannot be a master of all the machine learning skills with equal competency. So while it’s absolutely ok to set the bar high, don’t be disappointed if you don’t have expertise in all the skills that a machine learning engineer can/may use. Every machine learning skill can be honed over time by working on diverse machine learning projects. Use the 7 step road map to a machine learning career to plan out your learning. So without a further do here is the 7 step guide that will answer your question on how to become a machine learning engineer.

Before we forget, we want to make sure you know about our end-to-end solved data science and machine learning projects that are designed to help any mid-career professional kick-start their machine learning career. With that out of the way, onward with the 7 steps to becoming a machine learning engineer!

1) Is now a good time to become a machine learning engineer? (2021 Update)

Before you change careers, it is important to consider the path ahead. Can a career in machine learning offer you growth opportunities and stability? How favorable is the job market towards machine learning skills? How likely are you to get hired? These questions need to be answered especially in the wake of the 2020 pandemic as it has had a major impact on the economy and hiring trends. With that in mind, let’s take a look at the state of the machine learning industry in 2021 and beyond.

Machine Learning Engineer — The Hype is Real

You’ll have observed that, no matter what’s going on in the world around us, machine learning is omnipresent in our lives. Whether we’re trying to read and reply to our emails, scrolling through our Facebook news feed, binge-watching on Netflix, making a purchase on Amazon, or having a conversation with “Siri” to schedule an appointment — everything that we do today relies on machine learning. Behind this technology is a team of data scientists and machine learning engineers who have not only built smart applications but constantly maintain them to ensure these machine learning applications work flawlessly. Those who can build and deploy machine learning models have a crucial role to play in the data-driven world — and this is clearly reflected in the data science and machine learning job market.

Machine learning engineer is a pretty hot job title right now, and one which is set to become even more popular beyond 2021. Glassdoor ranked it 17 thin their top 50 jobs in America for 2021, stating 2977 new machine learning job openings. The World Economic Forum reported that AI, Machine Learning, and automation will power the creation of 97 million new jobs by 2025. According to LinkedIn as of February 8th, there are over 106K jobs worldwide that list machine learning as a required skill, and over 51K in the U.S. alone. The number of job roles in machine learning and artificial intelligence grew 344% between 2015 to 2018 (Indeed.com) — much faster than the average for all other tech job roles. According to Gartner, the business value created by AI and Machine Learning will reach $3.9 trillion in 2022.

But do these statistics still stand after the unpredictable twists and turns of the pandemic situation in 2020? In a word, yes; machine learning engineers seem to have weathered the storm relatively well. Machine learning engineers entered LinkedIn’s top 15 in-demand jobs for 2021, and we can see this continuing beyond 2021. One of the reasons for the growth in the AI and machine learning job market can be attributed to the COVID-19 pandemic where most of the businesses were forced to enter into the digital realm for the first time while other businesses were trying to strengthen and maintain their position. With an increased number of consumers spending more money and time online, machine learning has taken center-stage and has become an essential technology for building a world post-COVID.

2) What is a machine learning engineer?

Before you jump into learning machine learning skills, first off let’s get clear on what is a machine learning engineer actually. A machine learning engineer is a software engineer by profession and sits at the intersection of software engineers and data scientists but with a specialization in machine learning. The focus of ML engineers goes beyond particularly programming machines to perform specific tasks. They are responsible for the end-to-end implementation and optimization of machine learning algorithms. The final output of an ML engineer is a working software product ( not the visualizations or analytical insights created along the way), and the “audience” for this output are other software pieces that run with minimal or no human intervention. An ML engineer is responsible for handling the theoretical data science models and scaling them up for production levels so they can take a substantial amount of real-time data. To get a better understanding of who is a machine learning engineer, let’s look at what does a machine learning engineer does on a day-to-day basis.

What does a machine learning engineer do?

Implement statistical analysis and machine learning into highly available and high performant production level systems to provide ease of access to users.
Automate feature engineering, model training, and evaluation process.
Enrich machine learning frameworks and libraries.
Develop API’s or web services for serving the model outcomes to internal teams, stakeholders, or users.
Train and re-train machine learning systems as and when required.
Translate the machine learning models defined by data scientists from environments like Python and R notebooks to analytic applications.

3) Machine Learning Engineer vs Data Scientist

You might hear the terms data scientist and machine learning engineer used interchangeably but these are two different job roles. Talking at a high level, we are trying to differentiate between scientists and engineers and it is pretty much obvious that the two are different job roles. The job role of an ML engineer is very much close to that of a data scientist because both work with large amounts of data and require skills to perform complex data modeling. But this is where the similarities between the two job roles end and this sparks a debate on the topic of data scientist vs machine learning engineer.

There are several definitions floating around the Internet for both job roles but these two professionals work in collaboration with each other to realize a quick and effective delivery of business value. A data scientist produces meaningful insights usually in the form of reports or charts while a machine learning engineer develops self-running software to automate predictive machine learning models. An ML engineer’s job role is the subset of a data scientist’s job role. A machine learning engineer acts as a bridge between the model-building task of the data scientist and the development of production-ready robust machine learning platforms, systems, and services.

A major difference between a data scientist and a machine learning engineer is that a data scientist asks “What is the best machine learning algorithm to solve a given business problem?” and tries to find an answer to the question by testing various hypotheses. On the contrary, an ML engineer asks “What is the best system to solve the problem?” and finds a solution by building an automated process that can be used for speeding up the testing of hypotheses. A machine learning engineer feeds data into the machine learning models defined by data scientists. Both data scientists and machine learning engineers are of vital importance throughout the life cycle of a big data project and work together harmoniously to complement each other.

Data Scientist vs Machine Learning Engineer — Unleashing the Differences

Machine learning engineers forecast and make predictions based on historical data using various machine learning models.A data scientist deals with real-world complex data to produce actionable insights. Applies machine learning to build actual data products
The job of a machine learning engineer is experimental while the job of a data scientist is exploratory.
Key Skills required for a ML engineer are — Knowledge of Supervised/Unsupervised ML algorithms, NLP, Computer Vision, Deep Learning, Knowledge of Python, Tensorflow, Keras, PyTorch, etc, Data Wrangling, API’s, Algorithm Deployment, Scaling on Cloud, and Basic Math and Statistic concepts for ML. Key skills required for a data scientist are -Statistical Skills, Programming Languages, Big Data platforms, Machine Learning, and Data Visualization.
The major challenge for machine learning engineers is dealing with the algorithm’s complexity and its scalability. Thus, an ML engineer is required to know how to tune parameters.The major challenge for data scientists is dealing with the unavailability of data.
The average Machine Learning Engineer Salary: $112691 while the average Data Scientist Salary: $129,000

4) Learn Machine Learning Skills

The skills needed for a machine learning engineer are diverse. In order to build, deploy and evaluate machine learning models, ML engineers work with programming languages, machine learning frameworks, tools, and libraries. Let’s take a look at each of the machine learning skills in detail, that machine learning engineers use in their day-to-day work -

In the world of machine learning, programming languages are the building blocks that ML engineers use to develop machine learning algorithms. There are many programming languages like C++, Java, Python, R, Clojure, or even Scala. Choose any one programming language and master it. Remember, not knowing a programming language will never be a deal-breaker in your machine learning career because any programming language can be learned fast enough.

We suggest you focus on learning Python as it has become the de-facto programming language for the machine learning community. You will find thousands of lines of Python code that you can inspire to develop machine learning systems. In fact, most of the machine learning tools and frameworks (Keras, Tensorflow, Pandas, Sci-Py, Num-Py, Sci-Kit) used by ML engineers to develop machine learning systems are open-source. Apart from learning to program, you will need to know the basics of computer science fundamentals such as computer architecture, data structures, searching and sorting algorithms, and how to compute the complexity of algorithms.

When learning any programming language, these are the key points to learn -

Master the ability to build specialized data structures like binary trees, linked lists, or prefix trees.
Master the ability to make use of highly optimized vectorized operations rather than loops.
Handling exceptions.
Working with data structures like lists, maps, sets, dictionaries, and hands-on experience on when to use which data structure.

Free access to solved code Python and R examples can be found here (these are ready-to-use for your Data Science and ML projects)

It is not possible to imagine the machine learning ecosystem without Linux. Though Windows and Mac are also great alternatives but a successful machine learning engineer is required to know how to install Linux and other required python packages for ML, how to work with the Linux file system, and how to move or copy data from Linux OS. Be it speed or flexibility, Linux has it all that an ML engineer needs.

The nuts and bolts taken from the field of probability and statistics are needed for a machine learning engineer. Most of the common machine learning algorithms are an extension of statistical modeling procedure, For this reason, it is necessary to learn the basic concepts of probability and statistics like — Bayes net, Hidden Markov Models, Conditional Probability, Types of Distribution, Hypothesis testing, ANOVA, etc.

You will find several existing machine learning APIs, libraries, and packages like Spark MLib, Sci-Kit learn, Tensorflow, Keras, H2O, Theano, etc, that provide standard implementations for almost all machine learning algorithms. However, applying any of the machine learning techniques requires selecting the right model (SVM, KNN, Decision trees, etc.), choosing the right learning method, and an in-depth understanding of hyperparameter tuning to understand how the parameters affect the learning process of an algorithm. ProjectPro’s innovative ML projects are a great way to get exposure to diverse types of machine learning problems and their nuances.

The goal of a machine learning engineer is to train the best performing machine learning model possible, using the structure of the dataset. An ML engineer should also know how to choose the right evaluation strategy and error measures for a machine learning model.

ProjectPro’s machine learning projects are set up with a perfectly curated learning path to help you learn all the required skills you need to become a machine learning engineer in the industry. That means you could have a new machine learning engineer job before this year’s over.

5) Build a Machine Learning Portfolio

The weakest part of most machine learning resumes is the lack of experience working on diverse machine learning projects. If this is your resume, focus on building an awesome portfolio by adding some interesting ML projects. Every ML engineer needs an online portfolio that showcases their ability to apply machine learning to real-world problems. Ideally, a machine learning portfolio could consist of freelance projects that you’ve worked on or any other interesting ML projects that you’ve gained hands-on experience with.

Especially for people who are getting started in the industry, you’ll need to build a job-winning machine learning portfolio to become a machine learning engineer. One way to do that is ProjectPro, “the one-stop platform to do data science and machine learning projects.” If you’re new to learning machine learning, add a diverse set of projects to your portfolio that exhibits your expertise of machine learning skills such as NLP, Neural Networks, Distributed Computing, Data Modelling and Evaluation, Reinforcement Learning along with hands-on knowledge of machine learning tools and technologies like Python, R, TensorFlow, Keras, etc. All interesting machine learning projects-whether for recruiters or gaining experience -count.

6) Find the Best Machine Learning Jobs

There are lots of great job portals like LinkedIn, Indeed, and Glassdoor where you should invest some time in finding the right machine learning job based on your skills. Apart from this, there are specific job portals like ML Jobs List designed particularly for machine learning jobs. And, yes don’t forget to read the complete machine learning job description because sometimes the job description may not seem like a perfect fit for your skills but when you read the complete machine learning job description then only you know it is the dream job you’ve been looking for.

7) Ace Your Machine Learning Interview

Regardless of whether you’re attempting to land clients as a freelance machine learning engineer or you’re seeking a full-time machine learning job, here are some best practices to follow when preparing for a machine learning interview -

Machine learning interview questions function slightly differently than some of the other interview questions that you may have answered in the past. Choose a programming language preferably Python or R, master it, and prepare yourself to answer any kind of practical questions by writing code in a programming language you’re comfortable with. Here’s a list of machine learning interview questions to get you started.

Yes, the beauty of machine learning engineers is that they can handle the end-to-end development of a machine learning solution. But, every ML engineer has his own strengths, interests, and specialized skills. Chances are that the hiring manager will ask you whether you prefer working on NLP problems or love building deep learning models or have an affinity towards computer vision. Don’t be afraid to share your specialties, and show how you specialize in one skill versus the other.

Companies will ask you to whiteboard a business use case during your interview just to understand your thought process and how well you code and analyze real-world business problems. Make sure you’re prepared for solving a custom machine learning project. The hiring managers will set up a business problem they already are working on and might ask you to propose an optimal machine learning solution for the same. For example, say if you are being interviewed at Wayfair they might ask you broad questions like “How do we optimize our television advertising budget?”. The hiring manager here is expecting you to ask other relevant questions and initiate discussion on the various data sources you might need, the metrics you will need to track, and then talk about the machine learning algorithms that you can implement to solve the given problem. The best way to prepare for a whiteboard data challenge to ace the machine learning interview is to practice diverse machine learning projects and get exposure to as many datasets and machine learning concepts as possible.

Free access to solved code Python and R examples can be found here (these are ready-to-use for your Data Science and ML projects)

Getting Started

A machine learning model with 98% accuracy locked in a Jupyter notebook is of no use! Become a machine learning engineer and deploy it in production! All great machine learning engineers start the same way — with interesting machine learning project ideas. The question is, where do you begin? How do you go from having an innovative machine learning project idea to successfully implementing it? At ProjectPro we guide you through the early steps of learning and training a machine learning model across various domains, so you’ll gain knowledge of all the essential machine learning tools, skills, frameworks, and technologies to build a successful machine learning career with longevity.