Machine Learning Operations (MLOps) Engineering is an emerging career path that sits between DevOps and Machine Learning. Aimed at effectively developing, testing, and deploying machine learning (ML) models. While DevOps involves software development and deployment, MLOps follows the same process but primarily deals with machine learning models.
MLOps is a growing field due to the increasing use of machine learning in business decision making. A relatively new job, the role of MLOps engineer can vary from company to company as companies decide how best to incorporate machine learning operations. However, as the use of artificial intelligence increases in the coming years, there is no doubt that the need for MLOps engineering professionals will grow rapidly.
Learn more about the roles and responsibilities of an MLOps engineer and how you can pursue this promising career.
What is an MLOps Engineer?
MLOps Engineers leverage machine learning expertise in operational roles, collaborating with data scientists, developers, IT operations staff, and stakeholders. Effectively bridge the gap between these roles to bring ML models through the development, testing, deployment, and scalability lifecycle.
MLOps and DevOps
MLOps and DevOps are similar in that they both focus on operational procedures in an IT environment. However, while DevOps involves software development and deployment, MLOps focuses on developing, generating, training, and monitoring machine learning models.
MLOps Engineer Roles and Responsibilities
Because MLOps Engineer is a relatively new position, your duties and responsibilities may vary depending on where you work, where you work, and your company’s understanding of the MLOps process. Generally, the MLOps engineering role can be broken down into three key parts:
development:
-
Supervising ML model pipelines
-
Approving changes and reviewing features
-
Monitor test success
-
Ensure model artifacts are handled properly
introduction:
-
Train and test ML models
-
Using continuous integration/continuous deployment (CI/CD) techniques
-
Deploy ML models to production using tools like Docker and Kubernetes
Management and monitoring:
-
Data monitoring and reporting, preparation of required documents
-
Implementing automatic model retraining functionality
-
Track error rates, response times, and resources reporting anomalies using monitoring tools
MLOps Engineer and ML Engineer
MLOps engineers are responsible for the workflow and lifecycle for moving machine learning models into production. This is different from the role of an ML engineer, who is responsible for designing and developing ML models. Especially in smaller companies, these roles often intersect as the scope of responsibilities can be wide.
MLOps Engineer Skills
MLOps engineers require a combination of machine learning, development, and operations skills. This role involves highly technical capabilities, but also relies on collaboration and teamwork, so both technical and workplace skills are essential.
Technical skills:
-
machine learning algorithms
-
DevOps
-
data science
-
Workflow automation
-
CI/CD
-
software development
-
Agile method
-
Programming language: Python, C++, Java
-
software testing
-
statistical modeling
-
Database construction and management: SQL
Workplace skills:
-
collaboration
-
communication
-
organization
MLOps Engineer Job Outlook
Companies are experiencing a skills gap when it comes to MLOps and are struggling to recruit staff with the right machine learning skills. In fact, one in three IT leaders struggle to find the right employees for ML roles. [1].
The World Economic Forum predicts demand for artificial intelligence and machine learning specialists to increase by 40% by 2027 [2]. Being able to demonstrate relevant qualifications and experience in both DevOps and Machine Learning will help you stay ahead of the curve.
Additionally, the global MLOps market is expected to be worth more than $19.55 billion by 2032 (up from approximately $1.5 billion in 2024), indicating increased job opportunities for MLOps in the future. [3]. In industries that rely heavily on machine learning, the following are likely to see the greatest growth in MLOps employment:
-
banking industry
-
health care
-
manufacturing industry
-
marketing and sales
-
retail
MLOps Engineer Salary
According to ZipRecruiter, the average annual salary for an MLOps Engineer in the United States is $87,220, with top earners making $136,500. [4]. Although Glassdoor does not have salary information specifically for MLOps engineers, the average annual base salaries for Machine Learning Engineers and DevOps Engineers on Glassdoor are $156,000 and $139,000, respectively. [5,6].
How to become an MLOps engineer
MLOps is such a new field that you don’t necessarily have to follow the standard path to get into this profession, but it’s usually a senior-level role that requires a background in software development. Working in a role at this level typically requires a bachelor’s degree in a related major, such as computer science, data science, software engineering, mathematics, or statistics, as well as relevant experience in the field.
The more skills, education, and experience you can demonstrate, the more likely you are to secure a position. So consider building your credentials with online courses and certifications.
Career similar to MLOps Engineer
The skills and experience required to work as an MLOps engineer are useful in other similar careers, and vice versa. You can transition into this role from a DevOps role or a machine learning background, or one of the following careers can lead to an MLOps job.
According to the U.S. Bureau of Labor Statistics, employment in the software development field is expected to grow 17% from 2023 to 2033, with an average of 140,100 new jobs per year. [5]. Employment of data scientists is expected to grow by 36% over 10 years [6].
1. Machine Learning Engineer
Average annual income in the US: $56,000 [7]
Requirements: As a machine learning engineer, you may need a bachelor’s and master’s degree in data science, software engineering, electrical engineering, computer science, or a similar field.
Machine learning engineers design and build machine learning algorithms and models for automation. These models are a type of artificial intelligence with learning capabilities that develop over time and become more precise in their operations as they are retained and learned.
2. DevOps Engineer
Average annual income in the US: $139,000 [8]
Requirements: As a DevOps engineer, you may need a bachelor’s degree in computer science or a similar field. You may also consider earning a master’s degree to advance your career.
DevOps engineers will collaborate with both development and operations teams on software development and deployment. Working within the software development lifecycle automates and optimizes processes to ensure smooth operations and enhance cross-departmental collaboration.
3. Site Reliability Engineer (SRE)
Average annual income in the US: $165,000 [9]
Requirements: As a site reliability engineer, you may need a bachelor’s degree in computer science, software design, computer engineering, etc. Some employers expect a master’s degree.
SRE designs technical solutions to enhance system performance and ensure safety. To do this, use software tools to automate tasks such as application monitoring and make your systems more reliable and scalable.
4. Data Scientist
Average annual income in the US: $151,000 [10]
Requirements: Data scientists may require a bachelor’s degree in computer science, information technology, or a similar field.
As a data scientist, you can also work in an MLOps team. Your role may involve analyzing and using data, including building machine learning models. Create reports and create diagrams and charts to summarize data and present it to decision makers.
Coursera’s next steps to becoming an MLOps engineer
MLOps is an emerging field that functions within DevOps and machine learning roles. As machine learning becomes more popular, companies need processes to effectively develop and deploy ML models.
If you have DevOps experience and want to learn more about machine learning in preparation for a MLOps engineer role, consider the IBM Machine Learning Professional Certificate available on Coursera.
