Based on research conducted in 2019, the role of the Machine Learning Engineer won hands down as one of the fastest-growing and most desirable job titles. Indeed.com reports that the average base salary of a Machine Learning Engineer in the U.S. is $149,801 per annum, while Glassdoor claims the average salary to be $127,326 per annum. In the years to come, it is being predicted that companies will be on a hiring spree for machine learning professionals.
What is the Role of a Machine Learning Engineer?
A machine learning engineer, also known as ML Engineer, is basically an IT professional who works on self-running artificial intelligence systems. They are usually part of a larger team of data scientists, administrators and data analysts. Depending on the size of the company, they may also communicate with the IT, software development, sales, or web development teams. Machine Learning Engineers might also collaborate with other stakeholders, which includes senior business leaders as well as anyone with different skills, depending on their seniority.
The primary goal of ML engineers is to create machine learning models and retrain, when necessary. ML engineers are responsible for analyzing, organizing, and assessing large quantities of data. They also perform tests as well as optimize machine learning models and algorithms. In fact, these professionals create and design AI algorithms that can learn and make predictions.
Listed below are the core responsibilities of a Machine Learning engineer:
Designing ML systems
Researching and implementing ML algorithms
Selecting the right data sets
Picking appropriate data representation methods
Recognizing differences in data distributions that can affect model performance
Verifying data quality
Transform and convert data science prototypes
Perform statistical analysis
Conduct Machine learning tests
Use results to improve models
When necessary, train and retrain
Qualification and Skills of a Machine Learning Engineer
These are the basic qualifications and skills that are required for a career as a machine-learning engineer.
Advanced math and statistics skills in areas such as linear algebra, calculus and Bayesian statistics
Graduate degree in computer science, mathematics, statistics, or another related field
Master's degree in machine learning, neural networks, deep learning, or other related fields
Strong problem-solving, analytical and teamwork skills
Software engineering skills
Data science experience
Expertise in programming languages, including Python, Java and C++
Experience in using ML frameworks
Experience working with ML packages and libraries
Knowledge of data structures, data modeling, and software architecture
Computer architecture knowledge
Job Responsibilities of a Machine Learning Engineer
While the exact duties of each Machine Learning Engineer role will vary depending upon the organization's size and the data science team, a majority of them will have the following responsibilities:
Researching, designing and developing of Machine Learning models, schemes and systems
Studying, transforming and converting Data science prototypes
Selecting the right data sets and searching for them
Perform statistical analysis and use the results to improve models
Retraining and training ML models and systems as required
Identifying data distribution differences that could impact model performance in real-world scenarios
Visualizing data for deeper insights
Analyzing the use-cases of ML algorithms and ranking them according to their success probability
How your findings can be used to make business decisions
Enhancement of existing ML libraries and frameworks
Traits of Expert Machine Learning Engineers
Listed below are the traits of exceptionally talented Machine Learning Engineers.
They are solid computer programmers
Programming is a must if you want to work in AI or machine learning. Programmers should be familiar with common languages such as C++, Java, Python, and other programming languages. Machine learning has also made R, Lisp and Prolog important languages. However, not all machine learning engineers are experts in HTML and JavaScript.
They Have a Sturdy Foundation in Math and Statistics
Without some math, you can't master machine learning. You will need at least a high school level of math proficiency, regardless of whether you have a formal background or not in statistics and math. A formal description of probability is the core of many machine learning algorithms. This is closely related to the field of statistics which provides various measures, distributions and analysis methods necessary for validating and building models from data. Many machine learning algorithms are extensions of statistical modeling procedures.
They are Creative Problem Solvers
The best ML Engineers are driven by curiosity. They aren't frustrated when a model fails or an experiment topples. Instead, they are curious to discover why. They also solve problems quickly. Since fixing individual bugs in machine learning models can be tedious and time-consuming, the best machine learning professionals use generalized methods to fix them. You must also balance your determination to solve problems and the understanding that many of your experiments and models will fail.
They love the Iterative Process
Machine learning is, by its very nature, an iterative process. This style of development is essential to be successful in this role. In order to build a machine learning system, one must first create a simple model and then improve it step by step. A good Machine Learning Engineer cannot be too stubborn. It is important to know when it is time to stop. You can always improve any machine learning system's accuracy by continuing to iterate, but it is not worth the effort. One must learn how to recognize when it's time to stop.
They have a strong intuition about data
Machine learning is incomplete without data analysis. Machine Learning Engineers and Data Scientists must be able to quickly analyze large amounts of data, find patterns and use that data to make meaningful and useful conclusions. They almost have a sixth sense for data. They must also be able to build big data pipelines. You must also be able to visualize. You will need to be proficient with data visualization tools such as Excel, Tableau and Power BI so that your insights are understood and appreciated by others.
Job Roles Similar to that of a Machine Learning Engineer
Many data scientists work in similar roles as Machine Learning engineers within the wider field of data science. These are just a few of the possible positions for a Machine Learning professional.
Data Scientist: This role is at the intersection of technology and business. Data Scientists are expected to have the ability to see business problems and use data processing and analysis to find solutions. Data Scientists are responsible for identifying actionable insights hidden in unstructured data, and using that data to perform predictive analytics. They discover trends and patterns that help companies make data-driven business decisions, which can ultimately lead to increased revenue.
Data Analyst: They are responsible for the visualization or processing of data. Optimization is one of the most important skills or responsibilities of a Data Analyst. They create and modify algorithms that allow them to cull data without causing any damage.
Data Engineers: They build and test scalable big-data ecosystems to ensure Data Scientists have reliable and optimized data systems that they can use for their algorithms. A Data Engineer is also responsible for updating existing systems with the latest technologies.
Artificial Intelligence Engineer (AI Engineer): AI Engineers use traditional machine learning techniques such as natural language processing and neural network to create models that can power AI applications.
Computer Scientist: Computer scientists who work with computers and other computational systems deal mainly with software and their systems, including their theory and design, development, application and maintenance.
Software Engineer: Software engineers develop all types of software including operating systems, games, applications, and control systems. He/she is responsible for ensuring that active programs run smoothly and creating new programs, fixing bugs, and making updates.
On a final note, Machine learning is still a new field with many tools, algorithms and applications that are yet to be discovered. Like Software Developers, ML Engineers must also value learning. It is important to use blogs, tutorials and podcasts in order to keep up with a rapidly changing field. This is a great job for people who enjoy practical applications of math. A career as a Machine Learning Engineer offers a lot of variety and you could work in almost any industry by investing more time, money and resources in mining insights from data.
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