MLOps or Machine Learning Operations is gaining momentum in the data-driven job market, because of its crucial role in optimizing the deployment and management of machine learning models.
It sits at the intersection of the data science, machine learning, and DevOps and focuses on streamlining the process of taking machine learning models to production, and then maintaining and monitoring them.
For an even simpler explanation, MLOps is about maintaining the trained model’s performance when you have actually put that model into production.
It’s essential to emphasize that merely developing and deploying a trained model isn’t enough; ongoing maintenance is crucial. This is where the role of the MLOps engineer becomes indispensable.
With enterprises using MLOps as a separate process, the demand for MLOps engineers to deploy and manage ML models has increased.
We are sharing the roadmap to becoming MLOps engineer in 2024 if you’ve been exploring this lesser-known yet evolving field.
What is MLOps? Pipeline Explained
MLOps ensures seamless deployment of ML models, minimizing the risk of any issues or disruptions.
The pipeline starts with data engineering, data cleaning, feature selection, feature extraction, and a trained ML model is created out of it.
However, some factors can influence the performance of the trained model. For example, the data on which you’re running your model changes.
There can be some model drifts as well. So, the job of an MLOps engineer is to foresee such problems, report them, and roll out new models that are robust against all kinds of drifts.
MLOps guarantees that the deployment is flawless, swiftly addressing any issues that may arise during the process.
The model undergoes continuous monitoring during production to ensure it remains free from any issues. Unprecedented issues may occur after deployment, so, MLOps deal with it by following several analytics tools.
MLOps Engineer Job Responsibilities
MLOps engineers help in integrating the ML models into the organization’s existing infrastructure. The role of MLOps engineer also entails optimizing the ML models so that they can handle enormous amounts of data in a production environment.
These experts ensure that the machine learning algorithms are functioning well in production.
They work closely with the data science team to create new ML models and improve the existing ones from time to time without impacting the company’s system performance.
Take a look at this video to get the understanding of what is MLOps and why it’s required.
The responsibilities of the MLOps job are as follows:
- Working closely with the data science team to create and maintain platforms that can automate building, training, maintaining, optimizing, deploying, and updating ML models.
- Deploying and operationalizing MLOps, with a focus on:
- Optimizing model hyperparameters
- Evaluating and explaining models
- Automating and training models
- Model onboarding and decommissioning workflows
- Data archiving and version control
- Monitoring and governing the models and their performances.
- Developing and maintaining scalable MLOps frameworks
- Identifying ways to improve the ML models’ performance.
- Improving ML models’ accuracy by updating the data they use for training or tweaking their parameters.
- Troubleshooting issues with ML models whenever they arise.
Step-by-Step Roadmap to Become an MLOps Engineer
Build a Strong Educational Background
MLOps engineers require diverse skills from multiple fields. They should have knowledge of data science and, at the same time, must understand machine learning algorithms and possess critical software development skills. So, if you desire to start a career in MLOps in 2024, consider earning a quantitative degree in any of the following fields as your first step:
- Data science and analytics
- Computer science
- Data engineering
- Computational statistics
- Mathematics
Master Key Skills
The next step is building a robust knowledge base in the core MLOps engineer skills, from data science and programming to data management and DevOps field:
- Programming language
The primary responsibility of an MLOps engineer is to create and deploy the ML models and integrate them with the software programs. The first crucial skill you must possess is the knowledge of programming language.
The machine learning field mostly functions using Python as a programming language. So, master Python to deploy MLOps models and algorithms efficiently.
- Version control and CI/CD pipeline
Understanding the workings of version control systems can aid in smoothly running operations when running multiple ML model pipelines. Moreover, knowing how to integrate CI/CD with version control can also help potential candidates set up automated ML model training and testing processes.
- Machine Learning concepts
Next, you should have a good grasp of the key machine learning concepts, including supervised, reinforcement, and unsupervised learning. Moreover, being familiar with feature engineering and selection can help feed accurate data into the ML models.
To become an MLOps engineer, consider learning about key model evaluation metrics, like accuracy, ROC curves, precision, recall, and more.
- Database Management
Data Management is another vital and foundational skill. Every MLOps project involves data that needs to be collected, stored, and managed. Databases like SQL or NoSQL are considered the safest place to store data. Having basic and advanced knowledge and skills to handle these databases can prove to be helpful in your MLOps engineering career.
- DevOps
DevOps is all about practices that combine software development and IT operations to streamline the application development life cycle and deliver quality software continuously. Implementing MLOps is only possible by synergizing DevOps with ML workflows.
Furthermore, having an in-depth understanding of Linux commands can help in managing cloud-based infrastructure to deploy MLOps projects.
- MLOPs tools
The advanced technological developments present an array of tools that support MLOps operations, from MLflow and Comet to Databricks Lakehouse and Neptune.ai. Some of these tools help with data management, while others can streamline ML project lifecycles.
- Model deployment and monitoring
Deploying the ML models involves creating APIs or microservices, which other applications in a company can access.
And monitoring ML models is about pinpointing issues like performance degradation, bugs, and errors, and alerting on time for proactive troubleshooting. Mastering the know-how of model deployment and monitoring can provide you with a competitive edge against your counterparts.
Start Learning with Interview Kickstart
MLOps is still evolving and it is the best time to move your career forward.
Our MLOps Training Course is best suited for Machine Learning Engineers, Applied Scientists, and Data Scientists looking to revisit or master MLOps skills for pushing large-scale ML models to production.
How to Become MLOps Engineer: FAQS
Is MLOps a good career?
The demand for MLOps Engineers is set to grow exponentially because there are growing requirements to maintain the deployed machine learning models in the large industries. It is not a trend that will fade away in the future. It is rather a necessity for the seamless execution of ML models.
How long will it take to learn MLOps?
If you’re new to MLOps, expect it to take several months to hone all the requisite skills. Alternatively, if you’re already a DevOps engineer, your focus will primarily be on learning machine learning algorithms.
Is MLOps better than DevOPs?
Both the job profiles bring their own benefits based on the requirements of an organization. They both handle the operations to ensure the seamless workflow. DevOps focuses on traditional software development tasks, whereas MLOps focuses more on optimizing the deployment of ML models.
Both the fields are good, however, the demand for MLOps is growing following their requirements in the organizations that have fully or partially embraced AI solutions.
What’s the average salary of an MLOps Engineer?
The estimated total pay for a MLOps Engineer is $176,720 per year in the United States, with an average salary of $137,844 per year, as per the newest data by Glassdoor
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