Machine learning is a subset of artificial intelligence and is becoming an increasingly important aspect of a range of business consumer processes. From software automation to robotic production equipment, a machine learning engineer is usually the professional behind the programming and automation design.
If you're interested in machine learning, there are several things to take into consideration when pursuing a career in this field. In this article, we'll explore what machine learning is, what a machine learning engineer does and how to become one, with a glimpse into salary and how this career path compares to data science.
What is machine learning?
To understand the job of a machine learning engineer, it's important to understand the role of machine learning in technical applications. Machine learning is a subfield of artificial intelligence (AI). Machine learning engineers and data scientists use machine learning to automate a range of processes across industries, such as automated transactions and protections through a financial entity.
What does a machine learning engineer do?
Machine learning engineers are tech experts who design self-running programs that automate certain predictive objects or models. As these software programs perform operations over time, they retain those results to perform future operations with a higher degree of accuracy each time. Machine learning engineers design algorithms that identify patterns in data and learns from them. These professionals also perform tasks much like a data scientist would, where they'll work with large amounts of data to analyze, sort and integrate machine learning to carry out development projects.
Part data scientist and part software engineer, a machine learning engineer analyzes large amounts of data to predict or control results. Since machine learning is still an emerging field, machine learning engineers are often compared to (or confused with) data scientists.
Machine learning engineer vs. data scientist
Machine learning engineers and data scientists are both highly skilled professions, but machine learning is a newer field that is growing in demand. The ideal candidate for either of these professions has substantial knowledge of data analysis, advanced mathematics, advanced software engineering and programming languages. Considering how specific these skills are, there is sometimes crossover in these roles.
How they are alike: Machine learning engineers and data scientists both analyze big data to recognize problems and work out potential solutions and the necessary processes that come with achieving them.
How they differ: Machine learning engineers address these problems and processes specifically through the lens of software development. Data scientists will focus on analyzing statistics, whereas machine learning engineers think about how that data analysis will apply to the actual programming in terms of AI functions.
According to Indeed data, machine learning engineers earn an average salary of $150,134 per year, while data scientists earn an average salary of $123,046 per year. For the most up-to-date salary information, click the salary links provided.
Related: Highest Paying Data Scientist Jobs
What skills does a machine learning engineer need?
Machine learning engineers rely on a broad set of skills, many of which are highly specialized and technical in nature, though they must also be effective communicators with interpersonal skills. Their key skill set must include:
Computer science and programming languages
Strong computer and data science skills are of the utmost importance for machine learning engineers, with an understanding of applications like data structures, algorithms, complexity and compatibility between systems and computer architecture. A solid understanding of complex programming languages is also important, especially Python, although knowledge of other languages such as Java, R or SQL could prove useful as well.
Research and analysis
Strong research and analysis skills are necessary for performing many machine learning tasks. Engineers work closely with data scientists and analysts to perform data mining, sorting and categorization of massive amounts of information. Many automation and machine learning development projects start with research into user requirements, analyzing risk and implementing appropriate mitigation strategies.
Data evaluation and modeling
Machine learning engineers must rely on their knowledge of data modeling and evaluation to identify correlations and patterns and predict any properties of previously unobserved instances.
Machine learning principles and frameworks
Understanding standard machine learning algorithms is essential. Applying standard algorithms effectively within an appropriate model and creating learning procedures and parameters for automation are things you may do while on the job. You should also understand the most commonly used machine learning frameworks, such as TensorFlow, Spark or Keras.
System design and software engineering
Statistics and probability play an extremely important role in creating automated functions and software operations. Machine learning engineers work with complex mathematical and technical algorithms, which rely heavily on probability. For instance, machine learning engineers will apply methodologies like conditional probability and Bayes rule to inform the decision processes that make up the largest portions of machine-learning algorithms.
Additional soft skills
In addition to acquiring highly trained technical and analytical minds as mentioned above, there are some soft skills important in this field as well. In particular, good communication skills are required to relate complicated information in simpler terms, both in verbal presentations and in writing. Like most professionals managing complex tasks or creating programs, a machine learning engineer will also benefit from having project management skills.
How to become a machine learning engineer
Becoming a machine learning engineer requires hard work and a commitment to achievement. If you're interested in becoming a machine learning engineer, the following steps can provide an outline for getting started on your career path:
1. Consider an advanced degree
You could get started as an engineer with a bachelor's degree in data science, electrical engineering, software engineering or another related technology field. If available, you’ll want your concentration to include coursework related to machine learning foundations, applied data science, statistical computing and modeling, data mining and other engineering and machine learning principles.
However, you may want to consider continuing your degree through to the master’s level in the computer sciences, as you’ll want to demonstrate prowess in computer programming. While it is not technically required, the complex knowledge required to excel in this field of study would be well represented by an advanced education and make you more appealing to employers.
2. Gain work experience
You can gain experience through internships, entry-level positions and building portfolios of work with various applications of data and computer sciences. Build a portfolio of your work as you go, and aim to include various applications of your coding skills, architectural program designs, analysis and other machine learning uses.
3. Get certified in machine learning
Many machine learning engineers choose to get certified in various subfields like cloud computing, software engineering, data architecture or system design. These types of certifications demonstrate an engineer's expertise and can lend a competitive advantage during your job search. For instance, a certification in a specialization like TensorFlow can demonstrate your specialized skill set.