What exactly does a machine learning engineer do?

While the definition and thus the roles and responsibilities of a machine learning engineer varies from firm to firm, having the following skills helps - the ability to aggregate & clean data and perform data quality checks; the ability to build model pipelines, and the ability to deploy models to IaaS/PaaS.

Another thing to point out here is that the machine learning engineer role is different from a data scientist role. While the data scientist role these days focuses more on data cleaning, exploratory data analysis to understand the data and model building, the machine learning engineer role has a lot more 'software’'aspect attached to it as can be seen from the skills mentioned above.

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A machine learning engineer (ML engineer) is an IT professional who specializes in researching, developing, and constructing self-running artificial intelligence (AI) systems with the purpose of automating predictive models. Machine learning engineers design and develop AI algorithms that can learn and make predictions, which is what machine learning is all about (ML).

An ML engineer will often collaborate with data scientists, administrators, data analysts, data engineers, and data architects as part of a wider data science team. Depending on the scale of the firm, they may also communicate with employees outside of their teams, such as IT, software development, sales, or web development teams.

ML engineers serve as a link between data scientists who specialize in statistical and model-building work and machine learning and AI system development.

The machine learning engineer’s job include analyzing, organizing, and analyzing vast volumes of data, as well as running tests and optimizing machine learning models and algorithms.

Roles and responsibilities of a machine learning engineer

The construction of machine learning models and, when necessary, retraining systems are the key aims of an ML engineer. Responsibilities vary based on the organization, however some frequent responsibilities for this role include:

• Investigating and implementing machine learning methods and tools.
• Choosing the right data sets
• Choosing the best data representation methods.
• Identifying data distribution discrepancies that affect model performance.
• Ensuring the accuracy of the data.
• Prototypes for data science are being modified and converted.
• Statistical data analysis
• Experimenting with machine learning
• Adapting models based on the results
• Systems for on-the-job training and retraining.
• Extending machine learning libraries’ capabilities.
• Developing machine learning applications that are tailored to the demands of clients.

Skills required to become a machine learning engineer

A person who wants to work as a machine learning engineer should have the following abilities and qualifications:

• Knowledge of advanced math and statistics, including linear algebra, calculus, and Bayesian statistics.
• A master’s degree in computer science, mathematics, statistics, or a closely related field.
• A master’s degree in machine learning, neural networks, deep learning, or a field relevant to these fields is required.
• Excellent analytical, problem-solving, and team-building abilities.
• Knowledge of software engineering.
• Data science experience is a plus.
• Python, Java, C++, C, R, and JavaScript are examples of coding and programming languages.
• Working knowledge of machine learning frameworks.
• Working knowledge of machine learning libraries and packages.
• Have a thorough understanding of data structures, data modeling, and software architecture.
• Computer architecture knowledge.