Machine learningĀ is no longer a sci-fi concept, but an actual application of AIĀ 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 toĀ Indeed, job postings with the terms āmachine learningā and āAIā 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 engineerās earning potential. In fact, according to the sameĀ 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
With code libraries likeĀ TensorFlow,Ā PyTorch, and many more, smaller companies and startups are able to incorporate machine learning into their products.
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.
GoogleĀ is one of the leading companies working on machine learning and artificial intelligence research. SomeĀ notable projectsĀ that Googlers have developed using machine learning include:
- Flood prediction
- Earthquake aftershock prediction
- Developing the open sourceĀ TensorFlowĀ library
- A virtual assistant (Google Duplex)
- Augmented realityĀ (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 introducingĀ ethical standardsĀ into space.
Amazon
Ever heard of Amazon Web Services (AWS)? The cloud computing arm of Amazon is a huge part of its business (check out ourĀ blog postĀ 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:
- SageMakerĀ ā A service for developers to build, train, and deploy machine learning models at scale.
- LexĀ ā A conversational interface (AKA chatbot).This is what powers Amazonās Alexa device
- DeepLensĀ ā Programmable camera with the ability to deep learn (used as a machine learning training tool).
Apple
AppleĀ is another leading company that hires machine learning engineers, with concentrations spanning across five areas:
- Machine Learning InfrastructureĀ ā Build the systems that machine learning researchers work on.
- Deep LearningĀ andĀ Reinforcement LearningĀ ā Research supervised and unsupervised learning, game theory, and more.
- Natural Language ProcessingĀ and Speech TechnologiesĀ ā Work on Apple products like Siri, text-to-speech, and other NLP technology.
- Computer VisionĀ ā Develop image-processing software and deep neural networks.
- Applied ResearchĀ ā Work on research and development for the latest of Appleās secret prototypes.
AlthoughĀ FacebookĀ 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 userās timelines, they also are the parent-company for many other products that leverage machine learning, including Oculus VR.
Uber
Itās no secret thatĀ UberĀ has been developingĀ self-driving carsĀ for the past few years. But thatās only one of the ways that the company incorporates machine learning into their product. Uber has also used machine learning in the following areas:
- MichaelangeloĀ ā Uberās 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, itās important to have a background in computer science concepts.
Youāll need to know how to code and have a solid understanding of algorithms. Once youāve covered the basics, you can start gaining machine learning skills, like data modelling, quadratic computing, and partial differential equations.
Enrol today in UdacityāsĀ Machine LearningĀ Nanodegree to get started on your journey. 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.