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How to Become a Machine Learning Engineer

Table of Contents

What is a Machine Learning Engineer
Career Outlook
Machine Learning Engineer Salary
Becoming a Machine Learning Engineer
Courses Needed
Day in the Life
Licensure & Certifications
Certifications Matter

What is a Machine Learning Engineer

Machine learning is a field of study of statistical models and algorithms that machines use to perform tasks without specific human instructions or guidance. The machines can rely upon patterns and logical inferences rather than express or detailed steps. Many scholars regard machine learning as a separate part of the broader field of artificial intelligence. While the history of machine learning relates to the 1950s, the enormous capacity needed to perform this on useful scale has been a recent phenomenon (Brookings Institution, “What is Machine Learning).

The basis of machine learning is probability. Much of the substance of intelligence relies upon probability rather than reason or strict logic. Machines are very good a using probability. Machine engineers design systems and programming that equips machines to apply models and probability to think and learn much in the ways that the human brain does.

Career Outlook

The Department of Labor’s national employment survey does not rate the occupation of Machine Learning Engineer and a separate employment class or category. The career outlook for machine learning engineers is strong because it is part of the broader fields of data science and Artificial Intelligence. Data science is the field that includes Machine Learning Engineering.

The job outlook information is for the broader group of computer and information research science. The broader group outlook is strong with high growth forecasts for the decade ending in 2026. The median pay range for 2018 was approximately $118,000 per year.

Machine Learning Engineer Salary

According to Payscale.com, the occupation Machine Learning Engineer has an average salary of $110,000 per year. The typical package includes bonus participation and profit sharing at about $10,000 for each. There are not enough samples to project late-career income, but the rise from entry level at about $105,000 to mid-career at about $130,000 supports the role of experience as a source of career and income growth.

How to Become

There are many workable paths to a career in machine learning engineering. The qualifications for entry can be simple such as a high school diploma and a strong aptitude for mathematics. The formal education path uses a Bachelor of Science degree in software engineering or a related field. As undergraduates or after graduation, bachelor’s degrees with specific courses on machine learning topics can equip students for entry and advanced career roles. The below-listed items describe the typical formal education pathway.

  1. Get a bachelor’s degree in engineering with a major or concentration in software engineering.
  2. Get training and education in the field of Machine Learning Engineering.
  3. Get experience in Machine Learning Engineering.

The theory and training needed to be a specialized Machine Learning engineer can be a MOOC (Massive Open Online Courses) on Edx, Udacity, or Coursera. The UDACITY Machine Learning Engineer Nondegree is an example of a program that can prepare students for machine learning engineering careers. Hands-on experience is needed to build skills, confidence, and a portfolio of projects using real data. Much of the available information on this new and constantly changing field is in blog and podcast form. Students must develop the habit of searching and learning from these types of sources as well as formal education and training.

Courses needed

The list of specific courses would vary by the institution and training program used by the student. The list below describes some consistent categories or study areas.

  • Machine Learning- This essential area includes graphic models, deep learning, and statistical learning probability.
  • Computer Science and Math are fundamentals for data structure, programming, testing, and algorithm design.
  • Statistics – knowledge of random variables, probability, estimating, variance, and bias.
  • Signals and systems – electrical engineering including de-noising, transforming signals, Fourier transformations.
  • Systems and Computer Architecture
  • Specialized Knowledge of Domains- Bioinformatics would require some depth in biology, speech recognition would need audio and signal processing, Internet-based concerns will require user interfaces, and computational advertising would be valuable. Students must understand the context of their chosen field and specialization.
  • Machine Learning Engineers must work in elaborate computer environments. They tend to work in one or more of the current programming languages such as Python or Hadoop R.

Day in the Life

A Machine Learning Engineer spends his and her days in software architecture and design. They work with production engineers and managers. Their overall goal is to make data products work seamlessly in production settings. They must see the entire picture as an integrated unit. A machine learning engineer is a highly specialized type of data scientist. They have stronger software engineering skills that data scientists and work with engineers that maintain production systems.

Much of the effort of the Machine Learning Engineer involves creating machine learning algorithms that can create efficient delivery of desired results. They work to reduce complexity in models, improve models, and create greater precision and accuracy.

The typical day for a Machine Learning Engineer may be to perform data statistical and analytical analysis. They may respond to requests for solving problems that occur in production, production planning, or product development. They must frequently develop solutions and insights that they must communicate to clients and team members.

When data pipelines and data grow stale, machine engineers must re-make the models and other foundation elements. Machine engineers go deeper than monitoring applications. They must protect the operations against errors and form potential attacks from outside of the systems.

Licensure, Certifications and Continuing Education

There are no state licensing requirements for software engineering, data science, or computer architecture and design. Since there are no licensing requirements, there are few if any continuing education requirements that support initial and second phase licenses. The need for continuing education, knowledge, and professional growth is essential for a successful career in machine learning engineering.

Certifications Matter

The field of machine learning is new and evolving. Machine learning engineers respond to needs, create innovations and technological advances. The technological advances spur greater and more extensive needs. Forma education is quite important, and an advanced degree, such as a master’s degree is a practical and competitive advantage. Certifications can bring attention to an applicant’s strengths and respond directly to an employer’s needs.

Machine Learning Data Science Certification from Harvard University (edX), Machine Learning Certification by Stanford University (Coursera), and Data Science Specialization – John Hopkins University (Coursera) are three of the top-rated Machine Learning certification. Other sponsors include Google Cloud Platform (Machine Learning with Tensor Flow) and the IBM Machine Learning -Data Science Certification.

The courses are designed for various levels of knowledge and experience. For example, the Machine Learning Training A-Z™: Hands-On Python & R in Data Science (Udemy) requires a math aptitude and a high school level education. There are many similar certification courses that offer a quick method for gaining a strong foundation and possibly entering the field.

Courses and certifications have sponsors and leaders. For example, the highly regarded Stanford program features the work of Andrew Ng whose background includes Google Brain and Baidu AI group. When expanding knowledge, machine learning engineers and students must read works that reflect the state of technology and development. These may include blogs, podcasts, and social media platforms like LinkedIn.

Sources:

Brookings Institution “What is Machine Learning”- https://www.brookings.edu/research/what-is-machine-learning/

Bureau of Labor Statistics, Occupational Outlook Handbook, Computer and Information Research Scientists https://www.bls.gov/ooh/computer-and-information-technology/computer-and-information-research-scientists.htm

PayScale https://www.payscale.com/research/US/Job