Without the assistance of human experts, no technology can function effectively.
In order to automate predictive models, machine learning engineers conduct research, create, and design self-running software.
This article will concentrate on the responsibilities and skills of a machine learning engineer and the reasons to become a machine learning engineer. Let’s start!
Related Reading: Machine Learning Inference
Table of Contents
Machine Learning Engineer: What Is It?
A branch of computer science that focuses specifically on artificial intelligence is machine learning. It mimics how people learn by interpreting data through algorithms. The machine’s accuracy in learning should be increased, and it should give the user data based on that learning.
From video surveillance to smartphone facial recognition, machine learning encompasses it all. But it’s also used by companies that deal directly with customers to comprehend consumer trends and preferences and create direct marketing or advertising campaigns.
A user’s preferences, likes, and website posts are used by social media sites like Facebook to target advertisements at them. Similar to this, online stores like Amazon employ algorithms to recommend products to customers based on their prior purchases and viewing habits.
Responsibilities Of A Machine Learning Engineer
The project, business, and industry will all have a big impact on what an ML engineer is responsible for. The primary responsibilities of this role have been compiled after searching through numerous job boards.
Taking part in the creation of business-oriented machine learning applications. MLEs collaborate closely with front-end and back-end engineers to create AI-powered applications. To understand business objectives and how to accomplish them using machine learning, they also interact with product managers.
The creation and use of machine learning systems. Engineers who specialize in machine learning are primarily responsible for providing ML models to users. They create a scalable system called a machine learning pipeline to automate and manage ML processes. MLEs frequently need to write customized code to support model implementation and execution within a targeted environment.
MLEs participate in MLOps, or the production-level automation of the entire model life cycle, in large, data-driven enterprises. This includes all phases, including the initial model training, monitoring, and retraining against new datasets.
Examining and strengthening ML algorithms. Up to 50% of the MLE’s workday may be spent on this task. To accomplish it, the machine learning engineer must
- clearly understand business goals;
- supervise data acquisition and collaborate with a data engineer to verify data quality;
- participate in developing, training, and choosing models that will solve business problems;
- write tests to evaluate the robustness of the models;
- monitor models in production using metrics;
- develop validation strategies; and
- retrain models to obtain better results.
Making documentation for machine learning procedures. MLEs should ideally provide accurate documentation of machine learning procedures. This style of technical documentation aids in streamlining the ML development process.
Delivering advice to businesses. MLEs should use their research and observations to make suggestions about how to streamline and enhance the creation of ML solutions. Typically, engineers use data visualization tools to transform quantitative information into charts and graphs so that non-technical people can understand it.
Required Skills Of Machine Learning Engineers
There are particular skills and characteristics to start looking for when hiring a machine learning engineer. Here are some of the key traits of an effective machine learning engineer, despite the fact that their duties vary from business to business.
A strong mathematical and statistical foundation. At the bare minimum, MLEs must be comfortable with
- basic linear algebra as data for ML tasks is presented in the form of matrices and vectors;
- calculus to understand how machine learning models work; and
- introductory statistics and probability. The former is used to determine how likely future events are, whereas the latter is used to analyze past events.
Background in programming Programming languages like C++, Java, and Python should be second nature to a machine learning engineer. Additionally, they must be at ease using R, Prolog, and Lisp, which are all crucial for machine learning tasks. An MLE position requires both a track record of successful software engineering experience and a passion for good engineering practices.
Data-related knowledge. The foundation of machine learning is data. Therefore, a good machine learning engineer is knowledgeable about data structures, data modeling, and database management systems. Additionally, they are able to present their findings using visualization software like Tableau, Dash, or Power BI.
Proficiency with ML libraries and frameworks. This includes strong software programs like Keras, PyTorch, and TensorFlow. While males typically don’t build models on their own, as we’ve already mentioned, they still need to have a solid grasp of cutting-edge ML techniques like deep learning and neural networks.
A strong ability to solve problems. To correct flaws and errors in machine learning models, engineers must use a variety of techniques. Failures shouldn’t frustrate them but rather pique their interest in discovering why the model isn’t functioning properly.
Excellent interpersonal relations. The majority of job specifications include this type of skill as a necessary component. Engineers who specialize in machine learning, for instance, may have to explain ML principles to those who are unfamiliar with the subject. On top of that, they frequently work together and exchange ideas with other professionals, including data scientists, software and data engineers, and more.
Salary And Job Demand Of Machine Learning Engineers
The top job for a machine learning engineer in 2019 according to Indeed. 1 job in the In other, comparable polls that same year, the U.S. the same role was ranked among the top three spots. Around that time, Gartner also noted that organizations frequently struggle with AI initiatives due to a lack of technical expertise, process, tooling, and deployment knowledge, which also explains the need for the position.
According to Indeed, the average base pays for an ML engineer in the United States is as of 2021. is $149,801 per year, while According to Glassdoor, the average salary is less, at $127,326 annually.
Reasons To Become A Machine Learning Engineer
Here are some of the best reasons to become a machine learning engineer if you’re thinking about a career in data or AI.
Machine Learning Jobs Are Lucrative
Machine Learning Engineer was named the No. 1 position on Indeed. 1 job of 2019 for good reason: they make an average salary of $148,485 in the job site (which included demand and the expansion of postings in its ranking) places the U.S. first. The data from Indeed also reveals that in one of the country’s larger markets, one could make up to $200,000. Machine learning engineers in San Francisco reported earning an average salary of just under $200,000, while those in New York said they made an average salary of just under $170,000.
Opportunities For Continual Learning
The study of machine learning is a recent development. There are still a ton of ideas, resources, methods, and programs that need to be developed and uncovered.
Like software developers, ML engineers must value learning by nature. To keep up with a young and rapidly evolving field, it’s crucial to use courses, blogs, tutorials, and podcasts.
According to BrainStation’s 2020 Digital Skills Survey, 61 percent of data professionals enroll in in-person classes, while another 60 percent concentrate on workshops. Continuing education is unquestionably a staple of the industry.
They Live On The Cutting-edge Of Technology
Are you one of those people who is merely fascinated by technology and reads with glee about the most recent developments in artificial intelligence or computer applications?
Working on the newest and most cutting-edge technologies would give you the chance to make a real difference in the world. You’ll enjoy learning new programming languages for cutting-edge applications if you enjoy logic and coding.
Additionally, it’s a fantastic career for those who enjoy using math in real-world settings. You would probably be able to use linear algebra, calculus, probability, and statistics in your daily work as a machine learning engineer.
Demand For Machine Learning Engineering Skills Is High
Big data is attracting a lot of attention from businesses, and as a result, demand for data professionals in the labor market is at an all-time high.
Even reports of bidding wars over talent in the field of artificial intelligence have emerged as tech industry giants race to hire the best minds available.
According to a recent study by Robert Half on the future of work, 30 percent of U.S. managers said their company was currently using In the next three to five years, 53 percent of respondents said they planned to use AI and ML.
In other words, there is no indication that the robust job market will soon dry up.
Machine Learning Careers Offer Variety
If you easily get bored, a career in machine learning would offer a lot of variety. You could choose to work in any industry that interests you because almost every industry you can think of would stand to gain from devoting more time, money, and resources to extracting insights from data.
Additionally, you have the chance to actually alter the world. You might be a part of the group that creates the next big innovation in self-driving cars, cybersecurity, marketing, or healthcare. For many people, that’s a promising possibility.
Machine Learning Engineer Vs. Data Scientist
Given that both positions frequently involve handling large amounts of data, necessitating a certain set of qualifications, and employing comparable technologies, machine learning engineers and data scientists have roles that are comparable. Data scientists, on the other hand, draw meaningful conclusions from massive data sets, whereas ML engineers concentrate on building and managing AI systems and predictive models.
Obtaining, examining, and interpreting enormous amounts of data are the responsibilities of a data scientist. The vast quantities of data are used to create hypotheses, draw conclusions, and examine market or customer trends. For this job, you’ll need to be proficient in math, statistics, cluster analysis, and visualization, as well as advanced analytics technologies like predictive modeling and machine learning.
Utilizing various analytics and reporting tools to find patterns, trends, and relationships in data sets is one of a data scientist’s other fundamental duties.
Both machine learning engineers and data scientists will need to have a strong understanding of data management as they will collaborate frequently.