As artificial intelligence plays a growing role in our lives, the demand for machine learning engineers has skyrocketed. Read on to learn more about the skills needed to become an ML engineer.
Here’s what we’ll cover:
Machine learning engineering is a cornerstone of AI—without it, recommendation algorithms like those used by Netflix, YouTube, and Amazon; technologies that involve image or voice recognition; and many of the automated systems that power the products and services we use wouldn’t work.
That’s because machine learning engineering sits at the intersection of data science and software engineering; where a data scientist will analyze collected data to tease out actionable insights, a machine learning engineer will design the self-running software that makes use of that data and automates predictive models.
In action, if a machine learning engineer were to work on a recommendation algorithm for YouTube, they might be tasked with developing a machine learning pipeline that integrates into the platform so that a user can click on the “next” button once a video finishes playing and watch another video that is relevant to their interests, as determined by the data-informed algorithm and predictive models developed by YouTube’s data specialists.
The interdisciplinary nature of the role means that machine learning engineers are well versed in foundational data science skills such as understanding data structures and data modeling, quantitative analysis methods, building out data pipelines, and statistics, while also having computer science fundamentals and software engineering skills such as understanding computer architecture, proficiency with programming languages, and the ability to program software solutions.
Machine learning engineering combines software engineering principles with analytical and data science knowledge in order to make a machine learning model usable to a piece of software or person. This means that machine learning engineers need to have a slate of skills that span both data science and software engineering.
Soft skills are what set apart effective engineers from those who flounder. While machine learning engineering is, at its core, a technical job, soft skills such as the ability to clearly communicate, problem solve, manage time, and collaborate with others are what lead to a project’s successful completion and delivery.
In addition to a solid understanding of programming and scripting languages such as Python, SQL, Java, and C++, many machine learning engineers are also proficient in the following programs and tools:
Most machine learning engineering roles require a candidate to hold at least a bachelor’s degree in a related field such as computer science, mathematics, or statistics, and some require a master’s degree or Ph.D. in machine learning, computer vision, neural networks, deep learning, or a related field.
Beyond higher education, certifications in machine learning, artificial intelligence, or data science are useful because of the relevant skills they impart. There is no shortage of universities and online training programs offering machine learning certifications, each taking a different instructional approach and focusing on different facets of machine learning, so it’s important to examine closely the skills taught, the hands-on experience offered, and whether there are opportunities to work on projects that involve the full machine learning pipeline.
It’s also useful to cross-check program curricula against the requirements of recent machine learning engineering job listings. For example, a machine learning engineering role at Facebook requires a degree in a related field, plus experience with deep learning, reinforcement learning, privacy-preserving machine learning, large scale personalization, artificial intelligence, and experience with developing machine learning algorithms and infrastructure in C/C++ or Python.
Meanwhile, an Apple job listing requires a degree in a related field, plus experience with applying machine learning to solve real business problems, experience with cloud platforms such as AWS, experience with machine learning frameworks such as Scikit-Learn and TensorFlow, and experience with data processing.
The expectations placed on a machine learning engineer will differ depending on the organization and team they join. Whatever certification a candidate pursues, it’s important that it equips them with foundational skills that are transferable and can serve as the building blocks of their career.
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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