When we look at the job market related to AI as if it were a stage, the spotlight is often on programmers creating artificial intelligence algorithms and data science specialists. However, in this glow, an important specialization remains somewhat unnoticed, despite the growing demand for it. I want to dedicate some space to MLOps engineers. Without them, it would be difficult to talk about the effective application of artificial intelligence in real projects.
First, I want to emphasize one thing: the job market in the AI area is still in the process of forming, which means that often the same job titles in different companies may mean something different.
Demand for MLOps is Growing
MLOps (short for Machine Learning + Operations) is a discipline at the intersection of several fields:
- DevOps,
- machine learning,
- data engineering,
- and programming.
I’ll venture to simplify that MLOps serves as DevOps for AI models and data. It fills an important gap in the data lifecycle, the creation of AI models based on it, and the utilization of current data and AI models in applications.
The role of MLOps is unique as it combines skills from machine learning, software engineering, and system management. This makes it one of the most interdisciplinary in the IT market.
This field arises from a specific need for seamless integration of AI models, data, and applications in production environments. Moreover, it’s crucial to secure models and data to prevent leaks, ensure they are up-to-date, processed on the company’s infrastructure, and/or not used to train third-party AI models. MLOps, in accordance with corporate standards, not only accelerates the path from developing/updating a model to deployment but also ensures that the models remain efficient and operate stably over time.
Additionally, the growing demand for MLOps engineers aligns with the broader industry trends towards Agile methodologies and efficiency in IT teams.
MLOps Engineer Profile
An MLOps engineer must possess not only the technical expertise to implement, monitor, and scale models in production environments, but also data handling skills and knowledge of AI algorithms.
MLOps engineers usually have a wide range of responsibilities – from integrating machine learning models with existing data and application infrastructures to optimizing them in terms of efficiency and scalability. They ensure that systems are robust enough to handle data streams in real-time, making predictions and decisions that can significantly impact the operational efficiency and strategic direction of the company.
With the increasing demand for MLOps engineers, individuals in this position can expect some of the highest average salaries in the tech industry.
How to Become an MLOps Engineer?
To start working and succeed in this career path, a combination of solid theoretical knowledge and practical technical skills is required. I suggest 5 steps:
1. Solidify your knowledge in AI and data science fields
MLOps engineers should be well versed in key concepts and techniques of machine learning. For example, there will be needed knowledge about different types of learning (supervised, unsupervised, reinforcement), as well as understanding the data processing (including their structuring and cleaning) carried out by data science specialists.
2. Acquire skills in application development and DevOps
A deep understanding of DevOps practices, including process automation, containerization with Docker, and orchestration using Kubernetes, is crucial. Additionally, an MLOps engineer needs solid foundations in software development, demonstrating proficiency in popular programming languages such as Python or Java. Familiarity with at least one framework for developing AI models – PyTorch, TensorFlow, or Keras – is also recommended.
3. Get practical experience with MLOps tools
Practical knowledge of MLOps tools, such as MLFlow, Kubeflow, or Snowpark, will be crucial. I think it’s worth following job listings to see what companies are most often using. In addition, familiarizing yourself with the processes of building and maintaining data pipelines, managing the lifecycle of models, and monitoring AI’s operation in different environments will be important.
4. Develop other IT competencies
Additionally, knowledge of databases and cloud technologies (AWS, Azure, GCP) is crucial. An MLOps engineer often works with cloud infrastructure and microservices to scale and deploy AI models in flexible production environments. The type of data will define what database is used – so it’s worth knowing both relational and non-relational databases.
Furthermore, it’s necessary to navigate well in the Linux console (other systems are used less frequently) and know the basics related to network traffic.
5. Gain project experience
Real experience in the MLOps area can be gained through working on projects, both personal and commercial. Participation in open-source projects or internships can provide such opportunities.
One of the challenges for MLOps is ensuring that AI models are not only effective but also secure and compliant with regulations, which is crucial in sectors such as finance or healthcare.
It’s quite a lot to learn, isn’t it? Perhaps not everything needs to be known from the start. However, it’s worth integrating learning into a broader plan of your development or studies.
It can be said that MLOps teams bridge the theoretical and practical use of AI. This makes the role of an MLOps engineer both demanding and potentially very satisfying.
If you’re interested in this career path, continuous learning about AI (and there will be many more changes!) and gaining practical experience can start and develop your career in this segment of a job market.
The work of MLOps requires continuous learning and adaptation to rapidly changing technologies and tools.
If you’re a manager, it’s worth understanding what MLOps entails, as the job role will only become more prominent. Perhaps it’s time for you to recruit such a specialist?
I invite you to read the article about the job market in the era of AI and other articles on artificial intelligence. I also encourage you to subscribe to the newsletter, so you’ll receive information about new content and more! It’s worth signing up!
