Machine learning (ML) is the study of computer algorithms that can improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.
There are some key qualifications you’ll need to become a Machine Learning Engineer. Overall, this role is responsible for designing machine learning applications and systems, which involves assessing and organizing data, executing tests and experiments, and generally monitoring and optimizing the learning process to help develop strong performing machine learning systems.
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How to Become a Machine Learning Engineer in 2022?
Here are the few steps to become 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 17th in 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) How to Become a Machine Learning Engineer-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 –
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Programming and Computer Science Fundamentals
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 best 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.
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LINUX – The go-to OS for Machine Learning Engineers
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.
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Statistics and Probability
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.
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Know Your Machine Learning Algorithms
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.
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Data Modelling and Model Evaluation
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.
3) 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.
4) 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 , Relocate 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.
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5) 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
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Come Prepared for a Hands-on Coding 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.
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Come Prepared to Talk About Your Specialized Machine Learning Skills
Yes, the beauty of a machine learning engineer 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.
