Role and responsibilities: An ML engineer’s primary goals are the creation of machine learning models and retraining systems when needed. Responsibilities vary, depending on the organization, but some common responsibilities for this role include: designing ML systems, researching and implementing ML algorithms and tools, selecting appropriate data sets, picking appropriate data representation methods, identifying differences in data distribution that affects model performance, verifying data quality, transforming and converting data science prototypes, performing statistical analysis, running machine learning tests, using results to improve models, training and retraining systems when needed, extending machine learning libraries, developing machine learning apps according to client requirements.
Average salary (per annum): US$150906
- Advanced math and statistics skills, surrounding subjects such as linear algebra, calculus, and Bayesian statistics.
- Advanced degree in computer science, math, statistics, or a related degree.
- Master’s degree in machine learning, neural networks, deep learning, or related fields.
- Strong analytical, problem-solving, and teamwork skills.
- Software engineering skills.
- Experience in data science.
- Experience in working with ML frameworks.
- Experience working with ML libraries and packages.
- Understand data structures, data modeling, and software architecture.
- Knowledge in computer architecture.
Top 3 Online Courses:
Machine Learning, 4 Geeks Academy: Get trained by a renowned expert in A.I. Adoption; explore Machine Learning’s fundamentals and Deep Learning with some of the most powerful technologies that truly scale intelligence for business. Practice with real-life business scenarios typical in business and review some of the tools used in the industry. Strategize and develop your A.I. following the lifecycle development from start to end, including deployment into production and maintenance with Privacy, Security, and Ethics.
Learning Algorithms, Coursera: This course provides a broad introduction to machine learning, data mining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications so that you’ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
Machine Learning and Deep Learning, edX Courses: Machine Learning is the basis for the most exciting careers in data analysis today. You’ll learn the models and methods and apply them to real-world situations ranging from identifying trending news topics to building recommendation engines, ranking sports teams, and plotting the path of movie zombies.
Major perspectives covered include:
probabilistic versus non-probabilistic modeling
supervised versus unsupervised learning
Topics include classification and regression, clustering methods, sequential models, matrix factorization, topic modeling, and model selection.
Top Institutes Offering the Program:
Master of Science in Machine Learning: Carnegie Mellon University:
Computer Science M.S. with Specialisation in Machine Learning: Cornell University
Master of Science in Computer Science with Specialisation in Machine Learning: Georgia Institute of Technology
Top Recruiters for This Job:
Google: Google is one of the leading companies working on machine learning and artificial intelligence research. Some notable projects that Googlers have developed using machine learning include:
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.
Amazon: 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: 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.
Facebook: Although Facebook started 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 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.
Uber: 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 its product. Uber has also used machine learning in the following areas:
Michelangelo-Uber’s very own machine learning platform that helps developers create, train, and deploy models
User-demand and traffic prediction
Driver identity validation