Since there has been a rise in data-related job opportunities, a lot of people from different backgrounds have started to seek a career in this field. While most of them try to crack the job role in data analytics or data science, 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 over 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 recognises the need to adopt a technology, it automatically gets 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 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 at par with anyone in the room.
To get a better description of this expert, here is a list of their job responsibilities.
-Explainability and evaluation of models
-Governance and version control for models
-Monitor the model & its drift
-Optimize model hyperparameters
-Version control
Don't forget to read this- 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 in demand field that everyone is talking about. However, very less talk about MLOps, in spite of the fact that it offers similar payscale and has equally good career growth opportunities. Here are few reasons to choose this field-
The average salary of a machine learning operations engineer is around ₹12 LPA. There is a lot of scope of 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 because not there are not many experts in the field right now. Once your get MLOps certification, you will become an asset for any company who 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 read the following skills to master.
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 a 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.
It is important you 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 working with both NoSQL and SQL databases.
Becoming a machine learning operations engineer requires you to get certified. To make that happen, you can do a lot of things -
You can go for an MLOps certification titled - Certified MLOps Engineer Certification (CMOE-DS2160).
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. An online MLOps course is the best option today. It will give you the scope to learn from the best at your own pace and convenience.
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 MLOps tools include Flyte, Kubeflow, Pachyderm, mlflow, Metaflow, etc.
Ans. The 3 types of machine learning are unsupervised, supervised, and reinforcement learning.
Course Schedule
| Course Name | Batch Type | Details |
| MLOps Training | Every Weekday | View Details |
| MLOps Training | Every Weekend | View Details |
