In a data science project, creating machine learning models is not enough. It requires building pipelines for each process, deploying these pipelines to production, and managing the end-to-end process.
These steps can be performed using MLOps which stands for Machine Learning Operations. MLOps was started as a best practice that has slowly evolved into an independent approach to machine learning lifecycle management.
Companies are now using MLOps as a separate process which has created a new career opportunity for individuals who want to build their career in the analytics domain. The skills required to be an MLOps Engineer are easy to learn and can get you an equally high-paying job.
In this article, we will see how to build a career in MLOps, what all key skills are needed to be an MLOps engineer, and cover other related queries.
What is MLOps or AIOps?
The machine learning project lifecycle comprises of steps like data collection, data preprocessing, model building, model evaluation, etc.
The final step is the model deployment which is not a one-step process but includes a series of operations that demands specific skills, which are collectively called Machine Learning Operations (MLOps). MLOps focuses on automating the process of deploying machine learning models to production and then maintaining and monitoring them.
MLOps includes everything that comes after the machine learning model is built, including testing, logging, deployment, and scaling.
The word MLOps is a compound of two different fields i.e. machine learning and DevOps from software engineering.
Why is MLOps Required?
- The model created by data scientists needs to be deployed to production and integrated with a software application. This process requires individuals who have a good understanding MLOps technologies.
- Machine Learning models can face model drift issues over a period of time. It may require model retraining in which the complete pipeline is run again on new data. MLOps ensure the perfect working of ML pipelines.
- MLOps handles proper monitoring and maintenance of the deployed model.
The Demand for MLOps Engineers
The demand for analytics-related roles like Data Scientist, Machine Learning Engineer, Data Analyst, etc. is booming as the data is increasing.
MLOps Engineer is another highly demanding career option in the analytics field.  It is a high-paying career option that isn’t saturated yet. As per the report prepared by Cognilytica, the market for MLOps Solutions is expected to grow to almost $4B by 2025.
Hence if you want to be a part of the analytics industry, becoming an MLOps Engineer could be a great choice.
Difference Between DevOps and MLOps Engineer
- DevOps is used to manage the software development lifecycle whereas MLOps is used in implementing the end-to-end Machine Learning pipelines and putting them into production.
- The major difference between the two is that the software developed using DevOps is not degraded whereas the machine learning model deployed using MLOps may get stained over time (not performing well on new data). Hence in the case of DevOps, the updates are made on top of already-built code. On the other hand, MLOps may require you to change the code in the current pipeline.
Regardless of the differences, the eventual objective of both DevOps and MLOps is to generate top-quality code, faster updates and releases, and higher end-user satisfaction.
Pipeline comprises of the independent steps that are run in a sequence to achieve the final output.
Skills Required to Become MLOps Engineer
An MLOps engineer is responsible for model deployment and continuous maintenance. Below are the skills you should possess to be an MLOps Engineer.
1. Versioning Tools
In the process of software application development, a team is involved in which different members are working on different tasks. Versioning tools like Git are used to keep the track of changes. Similarly while working on a machine learning project, versioning tools can be used to keep track of modifications in the ML pipeline. Some common versioning tools used in machine learning projects are:
- Neptune
- Pachyderm
- DVC
- Git LFS (Git Large Storage File)
- Dolt
- LakeFS
- Â Delta Lake.
2. CI/CD Pipelines
CI denotes Continuous Integration, and CD stands for Continuous Delivery. Continuous integration allows team members to make updates in data, code and features lists. Continuous delivery helps in automating the process of deployment by eliminating manual workflows. Some common tools used for CI/CD pipelines are:
- GitHub Action
- Jenkins
- Circle CI
- CML (Continuous Machine Learning)
3. Cloud Technologies
Local systems cannot be used to train and deploy memory-intensive, complex machine-learning models. Cloud services can be used for these tasks. It has various advantages like it is cost-effective, having ease of data storage, and providing a better security system for machine learning models to prevent hacking and data breaches. Some common cloud platforms used are:
- Amazon Web Services
- Microsoft Azure
- Google Cloud
- IBM Cloud
4. Containerization
Sometimes the application developed by you works perfectly fine on your system but not on any other system. Every project requires a set of machine-learning libraries and other packages to be installed to work successfully. To solve this problem, a separate environment can be created for the project that can be switched to any machine. A few technologies used for containerization are:
- Docker
- Kubernetes
5. MLOps Tools
There are a variety of tools you can learn that support MLOps operations. Some of them are developed to handle specific areas like database management while others can handle the complete Machine learning project lifecycle. Some popular MLOps tools used are:
- MLflow
- Comet
- neptune.ai
- Databricks lakehouse
6. Programming Language
As an MLOps engineer, your task is to deploy the model and integrate it with the software application to make it available to the end user. For understanding the code written by data scientists, you should be good with programming. Python is the most widely used programming language in machine learning. Hence you can learn basic to intermediate Python.
7. Machine Learning
As an MLOps Engineer, you deploy the model build and manage the ML pipeline. If you have an idea about machine learning concepts like the feature engineering steps, the data types required, and the input and output at every step, it can ease your work.
8. Databases
You can never skip databases. Every project requires data that has to be stored somewhere. Usually, databases are considered to be the safest place to save data. It can be SQL or NoSQL database depending on the requirements. Having good hands-on basic and advanced SQL queries is a must-have skill.
9. Additional Skills
- Good understanding of Linux.
- Experience with software development.
- Ability to understand tools used by data scientists.
- Templating of machine learning pipelines.
- Command line, shell scripting.
How Much Coding is Required to be an MLOps Engineer?
Basic to intermediate knowledge of Python is required. You can focus more on libraries like pandas, and NumPy which are heavily used in machine learning projects.
What Educational Background is Required to Become MLOps Engineer?
There is no need for any specific educational background to be in MLOps. But if you come from a computer science, IT, Cloud, or analytics background, it becomes very easy to switch to MLOps.
Conclusion
MLOps takes into consideration that the model deployed is well maintained over time. It works as expected and does not have any adverse effect on business.
It makes machine learning operations (MLOps) a crucial part of projects. Hence the role of MLOps Engineer is in great demand and a good choice of career.
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Happy Learning!
