The last few years have been the witness to the rise in demand for data-related roles. There has been a significant upward change in the hiring demand for roles related to artificial intelligence and machine learning prowess.
The data industry is a highly attractive field and offers endless opportunities to begin. However, for those who wish to do something different, there are always more options. And of them is certainly becoming an MLOps Engineer. To better understand who they are and how to become one, it’s important to begin with what is MLOps.
What is MLOps?
The term MLOps is an amalgamation of machine learning and operations, which is a set of management techniques for the production ML lifecycle. These techniques incorporate DevOps and machine learning methods, along with data engineering processes to deploy and maintain ML models in production.
It has become a staple in organizations that realize the need to have the best means by their side. Some of the most often employed MLOps tools include Metaflow, Kubeflow, Flyte, and mlflow. Someone with the knowledge and skills to implement MLOps is an MLOps Engineer. Let’s understand who is an MLOps Engineer, and how to become one.
The Rise of MLOps Engineers - Who Are They?
A lot of organizations and industries realized that what they were lacking was an effective model deployment method. Hence, an entirely new function must be implemented by someone with the ability to work with both machine learning and operations. Thus, came the role of an MLOps Engineer!
An MLOps Engineer is someone with the knowledge of ML methods, an in-depth understanding of DevOps, and can carry out model deployment and ongoing maintenance. Moving ahead, we will discuss MLOps skills you need to become one.
What You Need to Become an MLOps Engineer?
There is a good set of skills you must acquire to become an MLOps Engineer. Here, we are going to give you a brief about each of those, followed by a point on how to gain them.
- The first thing you must be ready for when it comes to starting your MLOps journey is to begin by gaining knowledge of a good programming language. While there are a plethora of options with you, we suggest Python. It is a great language, especially for beginners, considering its multitude of features. Also, it is widely preferred by data scientists. You can also go for ones like C++.
- Working as an MLOps Engineer includes having extensive knowledge of how servers operate, and having an understanding of different operating systems, especially Linux. If you do not have a Linux OS, you can begin with a virtual computer like Ubuntu.
- Learn a scripting language to gain skills in automating procedures. Bash is an apt scripting language to begin with since it is a widely used one today.
- As an MLOps Engineer, you will be responsible for deploying ML applications to a production server. Hence, it is best to have extensive knowledge of model deployment.
- You will also need to have extensive knowledge of ML models and algorithms as you will be tasked with comprehending the models. Hence, it is important to know about frameworks used for creating these models, along with underlying ML techniques.
- To be called a capable MLOps Engineer, you should be familiar with databases. Collecting and storing external data in real time is a part of your job profile, and thus, important to know about. Which database is important to know about will depend upon your organization.
Additional Technical Skills
Here are some of the additional technical skills to have knowledge of, which will definitely help you in getting better job opportunities.
- Data Science
- Machine learning
- Statistical modeling
- SQL
- Python/ R programming
- Linux/ Unix Shell Scripting
Conclusion
The future of an MLOps Engineer looks bright and once you gain these essential skills, you will be able to get started sooner. Begin your tread towards a successful career by enrolling in the right MLOps training and learning from experienced trainers.
Image Resource:- Wikipedia
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