2022-10-22    Share on: Twitter | Facebook | HackerNews | Reddit

Is MLOps a good career?

Is a career in MLOps right for you? Learn about the pros and cons of this growing field, including high demand, high earning potential, exciting work, and career growth opportunities.

MLOps career

Machine learning is becoming an increasingly important field, and as a result, the role of MLOps is becoming more critical for organizations that are using machine learning in production environments. MLOps is a relatively new field, and as such, there is a high demand for skilled professionals in this area.

A career in MLOps can be rewarding, as you will be working at the forefront of technology and will have the opportunity to work on cutting-edge projects. MLOps professionals are in high demand and can expect to have a lot of job opportunities available to them.

Additionally, MLOps Engineers are typically well compensated and can expect to earn high salaries. According to Glassdoor, the average salary for an MLOps Engineer is around $120,000 per year.

Overall, a career in MLOps can be a good choice if you have a strong background in machine learning and programming, and are interested in working in a fast-paced, dynamic environment. It’s a field that is growing quickly and offers many opportunities for growth and development.

What are the good aspects of MLOps career?

There are multiple upsides of MLOps career, below, there are 7 of them listed and supported by arguments.

1. High demand

As the use of machine learning in production environments increases, so does the need for MLOps professionals. This high demand means that MLOps professionals can expect to have a lot of job opportunities available to them.

2. High earning potential

MLOps professionals are typically well compensated and can expect to earn high salaries. According to Glassdoor, the average salary for an MLOps Engineer is around $120,000 per year.

3. Exciting and dynamic work

MLOps is a relatively new field and is constantly evolving, so you will have the opportunity to work on cutting-edge projects and technologies.

4. Impactful work

MLOps plays a crucial role in ensuring the success of machine learning projects in production environments. By working in MLOps, you will be able to make a real impact by ensuring that machine learning models are deployed, monitored, and maintained effectively.

5. Variety of roles and industries

MLOps can be applied in a wide range of industries such as healthcare, finance, manufacturing, retail, and more. This means that you will have a wide range of options for where you can work and what types of projects you can work on.

6. Career growth opportunities

MLOps is a rapidly growing field, and as such, there are many opportunities for career growth and development. As you gain more experience, you may be able to advance to higher-level roles such as team lead or manager.

7. Continual learning

As the field of MLOps is continuously evolving and new technologies are emerging, MLOps professionals will have opportunities to continue learning and expanding their skills.

What can be the downsides of the MLops career?

While a career in MLOps can be very rewarding, there are also some downsides to consider. Please note that these are just potential downsides or challenges and you don't need to face them in your MLOps career.

1. High level of responsibility

MLOps professionals are responsible for ensuring that machine learning models are deployed, monitored, and maintained effectively in production environments. This can be a high-pressure role, as the success of the machine learning project relies heavily on the work of the MLOps team.

2. Complexity

MLOps is a complex field that requires a strong background in machine learning and programming, as well as experience with deployment, scaling, and monitoring. This complexity can make the field difficult to navigate for those without the right background or experience.

3. High stress

MLOps is a high-pressure field with tight deadlines and high stakes. This can be stressful for some professionals and may not be suitable for those who don't enjoy working under pressure.

4. Lack of standardization

MLOps is still a relatively new field and there is a lack of standardization in terms of best practices, tools, and methodologies. This can make it challenging to know how to approach specific problems and can make it hard to compare the performance of different teams.

5. Difficulty in finding the right team

MLOps requires collaboration between different teams such as machine learning engineers, data engineers, data scientists, DevOps engineers, and model governance. Finding the right team can be difficult and may take time.

6. Limited career mobility

MLOps is a highly specialized field, and as such, it can be difficult to transition to other roles or industries if you decide to leave the field.

It's important to keep in mind that while these downsides exist, they do not necessarily mean that a career in MLOps is not right for you. It's always recommended to weigh the pros and cons and see if it's the right fit for you and your goals.

Credits:

Heading image from Unsplash by Allec Gomes

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