Machine learning is no longer a sci-fi concept, but an actual application of AI technology we use every day. Machine learning engineers focus on developing computer programs that can access data and use it to learn themselves.
Their daily work involves helping machines learn by creating and fine-tuning training datasets, developing machine learning models, and testing these datasets and models on machines. The goal is for the machine to be able to make informed decisions without the direct instruction of a human.
Companies have recognized the value that machine learning can bring to their products and are scooping up people with machine learning expertise faster than schools can educate them.
According to Indeed, job postings with the terms “machine learning” and “AI” increased by 30% in the last year while people using those terms to job hunt went down by 15%.
This dip in qualified machine learning engineer candidates, combined with an increase in demand for these professionals, ultimately benefits the engineer’s earning potential. In fact, according to the same Indeed report, the salary for machine learning engineers has increased by 344% since 2015, with the average machine learning engineer making over $145,000 a year.
Top Companies Need Machine Learning Engineers
While this is a great step on the road to widespread machine learning adoption in the tech industry, most of the groundbreaking work in machine learning tends to happen at big companies.
- Flood prediction
- Earthquake aftershock prediction
- Developing the open source TensorFlow library
- A virtual assistant (Google Duplex)
- Augmented reality (in the Pixel 2)
In addition to all of the technology that Google has developed using machine learning, they have also paved the way for introducing ethical standards into the space.
Ever heard of Amazon Web Services ( AWS)? The cloud computing arm of Amazon is a huge part of its business (check out our blog post on AWS to learn more). Amazon machine learning engineers have developed a huge range of products leveraging artificial intelligence that are available on the cloud. Some of the most interesting machine learning AWS products include:
- SageMaker — A service for developers to build, train, and deploy machine learning models at scale.
- Lex — A conversational interface (AKA chatbot).This is what powers Amazon’s Alexa device
- DeepLens — Programmable camera with the ability to deep learn (used as a machine learning training tool).
Apple is another leading company that hires machine learning engineers, with concentrations spanning across five areas:
- Machine Learning Infrastructure — Build the systems that machine learning researchers work on.
- Deep Learning and Reinforcement Learning — Research supervised and unsupervised learning, game-theory, and more.
- Natural Language Processing and Speech Technologies — Work on Apple products like Siri, text-to-speech, and other NLP technology.
- Computer Vision — Develop image-processing software and deep neural networks.
- Applied Research — Work on research and development for the latest of Apple’s secret prototypes.
Although Facebook started out as a fairly simple social media application many years ago, it has grown to become one of the top tech companies in Silicon Valley.
Not only does Facebook use machine learning in their own product to translate languages, fight misinformation, and personalize their user’s timelines, they also are the parent-company for many other products that leverage machine learning, including Oculus VR.
It’s no secret that Uber has been developing self-driving cars for the past few years. But that’s only one of the ways that the company incorporates machine learning into their product. Uber has also used machine learning in the following areas:
- Michaelangelo — Uber’s very own machine learning platform that helps developers create, train, and deploy models
- User-demand and traffic prediction
- Driver identity validation
Get the Skills to Become a Machine Learning Engineer
To become a machine learning engineer, it’s important to have a background in computer science concepts.
You’ll need to know how to code and have a solid understanding of algorithms. Once you’ve covered the basics, you can start gaining machine learning skills, like data modeling, quadratic computing, and partial differential equations.
Enroll today in Udacity’s Machine Learning Nanodegree to get started on your journey or our Enterprise Training Solutions designed to upskill employees and teams. At 10 hours a week, you can finish the program in as little as three months, while also getting experience working on real-life projects, including a plagiarism detector.