This guide 'how to become MLOps engineer' will help you understand the core concept of machine learning operations and provide you with tips and tricks to become one.
With a rise in data-related job opportunities, many people from diverse backgrounds are seeking careers in this field. While most of them try to crack the data analytics or data science role, there are many other, lesser-known but more lucrative job titles. One such name is MLOps.
This blog is all about exploring everything about this sector to help you land a job for a successful future. How to become an MLOps engineer? This is the most searched question on the internet. Let's begin.
MLOps is a compilation of management techniques for the production or deep learning of the ML (machine learning) lifecycle. It stems from the combination of two words - ML from machine learning and Ops from operations.
MLOps or Machine Learning Operations technology offers a scalable, managed way of monitoring and deploying ML models in production environments. These best practices are being used by businesses to successfully run AI.
For Machine Learning Operations to have become this popular, it must be equipped with a lot of features. Here are a few of them -
When an industry recognizes the need to adopt a technology, it automatically leads to the need for an expert. And this is where the rise of MLOps experts has increased. There is a high demand for someone who can manage the workflow that occurred as a result of the model that was built.
Let us take a closer look at MLOps engineers and how to become one.
MLOps Engineers are professionals who optimize, build, and maintain ML solutions. It's their job to ensure the streamlined working of algorithms.
For those who wish to work as this engineer, it is important to be familiar with the ML methods. They must possess a wide skill set and their knowledge must be on par with anyone in the room.
To get a better description of this expert, here is a list of their job responsibilities.
Read Also- The Future of AI Deployment: An Introduction to MLOps
The demand for those with a talent in data-related fields is experiencing an all-time high.
Data Science is a high-demand field that everyone is talking about. However, very little talk about MLOps, in spite of the fact that it offers a similar pay scale and has equally good career growth opportunities. Here are a few reasons to choose this field:
The average salary of a machine learning operations engineer is around INR 12 LPA. There is a lot of scope for increasing this number with experience, skills, and the company you work with.
A majority of businesses are still learning their way around using and understanding MLOps. This has led to a direct impact on the demand for professionals in this field.
The demand for these professionals is high not because there are not many experts in the field right now. Once you get MLOps certification, you will become an asset for any company that wishes to use this technology. Thus, there is a great growth scope.
If you want to become a Machine Learning Operations Engineer, then it's important to follow the steps given below.
While there is no formal qualification requirement to become an MLOps engineer, a strong educational background is generally preferred. Organizations usually seek candidates with a bachelor's degree in computer science, software engineering, data science or any other related field.
The only necessity is a strong understanding of mathematics, statistics and programming. However, experience in ML and software development practices might also be a plus point.
Mastering the essential skills is the crucial step of this learning journey. These include:
Python is the most commonly used language by data scientists and you can choose to learn it too. Additionally, you can go for C++ as it has a rich ML library support and a fast runtime.
Learning a scripting language is important as it will lead you to automate processes. Python, Ruby, and Go are popular scripting languages employed for driving automation when deploying ML applications. Bash is the most widely used scripting language, and you can decide to go with it too.
You must learn about frameworks that are used to build models. An understanding of the basic ML algorithms is also needed. To get there, it is advisable to begin with frameworks. You can build supervised and unsupervised learning models with different frameworks such as Scikit-Learn in Python. Then you can move ahead to deep learning frameworks like Pytorch, Tensorflow, and Keras.
You must know how servers work. Knowledge about the working of different operating systems (OS), especially Linux. In case you do not have Linux OS, a virtual machine like Ubuntu can be downloaded.
Knowledge about working with databases is a must. You will have to learn about creating databases that can further collect and store external data, all in real time. It is best if you learn to work with both NoSQL and SQL databases.
Learning resources play a vital role for beginners aspiring to become MLOps engineers. These resources provide a comprehensive understanding of core principles, tools and best practices used in this technology. There are multiple resources available including tutorials, YouTube videos, online courses, training programs, etc. You will also get learning and guidance from the top experts of the industry through online courses.
Once you have enough knowledge in this field, start implementing your learning on real-time projects. You can also join an internship to know how your learnings will be implemented in real-world scenarios. This experience will be beneficial in two ways:
Becoming a machine learning operations engineer requires you to get certified. You can go for the following credentials:
Joining online communities offers valuable learning, networking and career opportunities for aspiring individuals. These communities provide access to resources, mentorship and a collaborative environment for skill development and career exploration. They also serve as a platform for finding job openings and connecting with industry professionals.
The last step is to prepare for the interview. Employers always seek the best candidate to hire for their organizations. Therefore, you have to maintain your resume and brush up on technical skills before appearing in one.
By following the above steps, you will have many things to decorate your resume like experience and certifications. Further, use interview question guides to understand the areas most commonly asked in the interviews.
These engineers earn impressive salary figures due to the high demand. Here is a glance on their salaries in different countries:
In this blog, we have talked about MLOps, the rise of this profession, and how to become one. Begin your success journey now by choosing the right MLOps course to learn the right tools and platforms.
Ans. Azure MLOps offers different management and monitoring capabilities like model deployment, model performance monitoring, and model versioning. AWS MLOps offers different tools to manage and monitor ML models like AWS Step Functions and Amazon CloudWatch.
Ans. The best MLOps platform includes MLFlow, Databricks, Azure Machine Learning, and DataRobot.
Ans. Top tools include Flyte, Kubeflow, Pachyderm, mlflow, Metaflow, etc.
Ans. The 3 types of machine learning are unsupervised, supervised, and reinforcement learning.
Gain expertise in ML, DevOps, cloud computing, and automation tools to start your journey.
Learn DevOps first, then gradually build ML knowledge through courses and hands-on projects.
Key skills include ML lifecycle management, CI/CD, containerization, cloud platforms, and monitoring.
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Course Schedule
| Course Name | Batch Type | Details |
| MLOps Training | Every Weekday | View Details |
| MLOps Training | Every Weekend | View Details |
