Machine learning is a rapidly growing field that is integral to the development of artificial intelligence. Learn more about how to become a machine learning engineer here.
Here’s what we’ll cover:
Whenever you’re browsing film and television recommendations on Netflix, encountering ads on social media that are relevant to your interests or search history, or voicing commands to Amazon’s Alexa or Apple’s Siri, you’re directly interacting with the work of a machine learning engineer.
While it takes a lot of dedication to gain both the data science and computer science fundamentals needed to become a machine learning engineer, there is a rewarding payoff: machine learning engineers are part of a rapidly evolving field that works at the forefront of deep learning and artificial intelligence and have a growing impact on the efficiency and innovation of industries ranging from entertainment to retail, healthcare, finance, tech, and auto.
Read on to find out more about how to become a machine learning engineer.
A machine learning engineer is a computer programmer who designs and builds self-running software that learns from data and automates predictive models. Because of the interdisciplinary nature of the job—needing both an understanding of data models and data structures and the ability to deploy those models in usable software—machine learning engineers sit at the intersection of software engineering and data science, possessing skills from both disciplines.
For this reason, machine learning engineers are in high demand across all industries that are heavy on automation, rely on big data, or are searching for ways to make their systems and services more efficient.
Machine learning engineers teach software and systems to learn on their own without human intervention. Think about YouTube and Netflix’s recommendation engines; Amazon’s purchasing suggestions; social media apps and platforms being able to detect spam or inappropriate content on their own.
In action, this involves performing dozens of different tasks, such as running machine learning experiments using programming languages and machine learning libraries; deploying machine learning solutions into production; optimizing those solutions for performance and scalability; implementing custom machine learning code; performing foundational data science work such as analyzing data and coming up with use cases; and performing foundational data engineering work by ensuring a good flow between databases and backend systems.
A large part of the job also requires collaboration with other stakeholders such as data scientists and researchers, software engineers, and product managers to define project goals, roadmaps, and how each professional’s work can inform the work of others.
While job descriptions for machine learning engineers will vary depending on industry, organization, and team, a typical machine learning engineering job description will emphasize the need for someone who can design and train computers to learn automatically.
Underscoring this skill set is a background in both data science and software engineering.
On the data science front, expected skills include proficiency with programming languages such as Python, SQL, and Java; the ability to perform hypothesis testing; data modeling; proficiency in mathematics, probability, and statistics (such as the Naive Bayes classifiers, conditional probability, likelihood, Bayes rule and Bayes nets, Hidden Markov Models, etc.), an understanding of variance, correlations, and dynamic programming, and being able to develop an evaluation strategy for predictive models and machine learning algorithms.
On the software engineering front, expected skills include proficiency with system design; understanding data structures such as stacks, queues, graphs, trees, and multi-dimensional arrays; understanding computability, complexity, and approximate algorithms; and knowledge of computer architecture such as memory, clusters, bandwidth, deadlocks, and cache.
Learn more about a machine learning engineer job description, skills, and more here.
Machine learning engineering is a relatively new and constantly evolving field. Because of this, there is no 'right' way to become a machine learning engineer. There are multiple ways to get into the field depending on your educational background, technical skills, and areas of interest.
The steps below outline how you can get hired as a machine learning engineer.
Want to know how to get into machine learning engineering or a related field? Springboard’s Machine Learning Engineering Career track is comprehensive, production-focused, and designed for people with strong software engineering or computer programming skills who want to become machine learning engineers.
The online, six-month curriculum will help you master key aspects of machine learning engineering such as machine learning models, deep learning, computer vision image processing, the machine learning engineering stack, and working with data. Most importantly, 50% of the course is focused on production engineering skills, which ensures that you not only graduate with a solid understanding of machine learning and deep learning concepts, but you also get the hands-on experience that hiring managers value.
In addition, you will learn to:
As with all of Springboard’s courses, you will also receive unlimited one-on-one support from an industry mentor, get experience working on real-world projects, and have access to career support and a job guarantee in a machine learning engineer role after completing the course.
Want to know more about how to get into machine learning engineering? Read on to find the answers to some frequently asked questions.
The first requirement is to have a strong grasp of computer science and data science skills, which means learning programming languages such as C++, Python, R, SQL, and Java, and tools such as MapReduce, TensorFlow, and Spark.
You should also be familiar with both the concepts and application of statistics, mathematics, neural network architecture, signal processing techniques, data structures, memory management, and AI training.
Machine learning engineering offers strong career stability and varied opportunities because it is in such high demand across multiple industries—the profession saw a 344% increase in job listings from 2015-2018, and this number is expected to rise in the coming years as more organizations realize the potential of marrying big data with software.
While it is possible to learn and understand some machine learning concepts without dabbling in code, a machine learning engineer who wants to implement machine learning models that tackle real-world problems will have to have a strong coding background. In fact, even having basic programming knowledge will open doors in machine learning because it will make accessible graphical and scripting ML environments such as Weka, Orange, and BigML, as well as machine learning libraries, which will allow you to perform complex tasks without having to write too much code.
Machine learning engineering is a high-paying, in-demand profession that is seeing rapid job growth and generous salaries.
Indeed ranked the profession number one in 2019 based on the number of open roles and the average compensation—344% growth in job postings from 2015-2018; an average base salary of around $146,085—describing it as an “extremely promising position.”
In addition to job security, machine learning engineering offers enormous variety and industry flexibility because machine learning engineers are needed across many different sectors, from government and healthcare to entertainment and tech, to finance and retail.
Most machine learning engineering positions require at least a bachelor’s degree or master’s degree in computer science, mathematics, statistics, or a related field. But the key determining factor in whether a person lands a machine learning engineering role is whether they have the knowledge, experience, and portfolio of projects to prove that they can get the job done. For this reason, it is not unheard of for a candidate without a degree to get hired if they can show that they have relevant experience.
It’s also not uncommon for candidates who hold degrees in unrelated fields to retrain—either through short courses, online bootcamps, or self-study—to pick up the relevant skills and experience and begin their careers in machine learning engineering.
It takes approximately six months to complete a machine learning engineering curriculum. If an individual is starting without any prior knowledge of computer programming, data science, or statistics, it can take longer.
Springboard’s Machine Learning Engineering Career Track takes 6 months to complete. Springboard’s Data Science Prep Course, which teaches foundational programming and statistics, takes 4-6 weeks to complete.
Becoming a machine learning engineer requires commitment. The role is multidisciplinary, requiring the technical development skills of a software engineer and the analytical skills of a data scientist.
Those who have a background in computer science, artificial intelligence, software development, statistics, data science, or data engineering will have a head start, but it is not uncommon for individuals to begin from scratch and be working in machine learning within a few years. After all, machine learning and artificial intelligence are relatively new and frequently evolving fields, which means there’s always more to learn—and plenty of room for newcomers.
Is machine learning engineering the right career for you?
Knowing machine learning and deep learning concepts is important—but not enough to get you hired. According to hiring managers, most job seekers lack the engineering skills to perform the job. This is why more than 50% of Springboard's Machine Learning Career Track curriculum is focused on production engineering skills. In this course, you'll design a machine learning/deep learning system, build a prototype, and deploy a running application that can be accessed via API or web service. No other bootcamp does this.
Our machine learning training will teach you linear and logistical regression, anomaly detection, cleaning, and transforming data. We’ll also teach you the most in-demand ML models and algorithms you’ll need to know to succeed. For each model, you will learn how it works conceptually first, then the applied mathematics necessary to implement it, and finally learn to test and train them.
Find out if you're eligible for Springboard's Machine Learning Career Track.
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