We breakdown the responsibilities for ML Engineers & MLOps Engineers highlighting where they overlap and where they differ.
In the field of machine learning the roles of ML Engineer and MLOps Engineer are often confused. While there are some overlaps in their responsibilities, as well as some distinctions, it’s key to delineate the small details that make these two functions different.
Disclaimer: At Tenyks we were attracting a lot of amazing people that we wanted to hire, but they didn’t have the MLOps engineering background we were looking for! Instead, they had a stellar ML engineering profile.
In this article, we will clarify the similarities and differences between these two roles. ML Engineers are primarily focused on building, training and optimizing machine learning models, while MLOps Engineers are mainly concerned with testing, deploying and monitoring ML models in production environments.
Gaining a good understanding of how these jobs differ and align will help ensure you pursue the right career path.
🔥 We’re hiring! Following our own advice, we have called it an “MLOps role VS an MLEng role” See available positions here!
Table of contents
- Why are these roles often confused?
1. Why are these roles often confused?
Here are three reasons why the ML Engineer and MLOps Engineer roles are often confused, along with the assumptions behind them:
- Their job titles sound similar - As naive as it might sound, the titles “Machine Learning Engineer” and “MLOps Engineer” are quite comparable -containing almost the same words. This can lead to the assumption that the roles themselves are nearly the same, when in fact they are quite distinct.
- Their work involves overlapping technologies - Both roles employ machine learning, software engineering, and cloud computing tools and techniques. The assumption here is that if the technologies are the same, the jobs must be the same. But how those technologies are applied is very different.
- Some recruiters might not fully understand the differences - P.S. Speaking from experience, this actually happened to us! Hiring recruiters, new to ML, without previous background in these domains may not understand the nuances between the positions. The assumption is that they believe they can hire for either role to accomplish their goals, not recognizing the specific skills and experience truly needed.
There are some responsibilities where there might be an overlap between the ML engineering and MLOps functions. This set of similarities is where often people get confused the most.
1. Model Validation - ML Engineers and MLOps Engineers share the responsibility of validating machine learning models, but they concentrate on different parts of the validation process. ML Engineers validate models during development, while MLOps Engineers validate models in production environments.
2. Stay updated with the latest information in the field - ML Engineers and MLOps Engineers have a shared duty of continuously learning and improving their expertise in ML and operations, but they may prioritize different skills and topics. ML Engineers may focus more on ML algorithms and frameworks (e.g. Tensorflow), while MLOps Engineers concentrate on deployment, monitoring, and workflow tools.
3. Cross-functional work - ML Engineers and MLOps Engineers regularly work together with various cross-functional partners, though the specifics of these interactions often differ. For example, ML Engineers work with data engineers on data acquisition and preparation, while MLOps Engineers work with IT and software engineers on deployment infrastructure.
4. Security and compliance - Safe, fair, and regulation-compliant ML requires oversight from both those who build models and those who deploy them to production. ML Engineers and MLOps Engineers put in place the checks and balances to achieve that.
Beyond the similarities in tasks between ML Engineer and MLOps Engineer, there are clear responsibilities for each role.
- Design and build machine learning models - Functionally, the engineer should be capable of designing, implementing, and training ML algorithms on a given dataset. The desired outcome is to provide predictive, prescriptive or descriptive insights which will aid business decision making.
- Preprocess Data - In a functional sense, this involves the application of data cleaning techniques and preprocessing methods to ensure data is suitable for ML models. The desired outcome is to improve the accuracy and reliability of the ML model’s predictions and analyses, hence reducing error in decision making.
- Feature Engineering - The job of feature engineering involves selecting, transforming or creating the right input features for machine learning models. Functionally, this improves the performance of ML algorithms. The desired outcome is to enhance the accuracy and generalization of the model, increasing the value and reliability of its predictions.
- Performance Tuning of ML Models - The ML engineer should functionally be able to tune the hyperparameters of the ML model and employ techniques to enhance model performance. The desired outcome is the improved accuracy of the model, leading to better business decisions or enhanced automated systems.
- Communicate Findings - Functionally, the ML engineer needs to communicate the results, model decisions and limitations to stakeholders. The desired outcome is to ensure transparency, drive business decisions, and facilitate knowledge sharing across the team or organization.
- Responsible AI - Functionally, this involves understanding the implications of bias, fairness, and transparency in machine learning models. The desired outcome is to build models in a responsible approach, meaning models that are fair, accountable, transparent, and avoid unintended bias, leading to a more trusted AI, brand, and improved customer relationships.
- Deploy and maintain pipelines: An MLOps engineer functionally ensures the smooth transition of ML models from the development phase to the production phase. The desired outcome is to ensure the efficient and error-free deployment of machine learning models, ultimately enabling businesses to use these models in real-time decision-making.
- Implement Continuous Integration/Continuous Deployment (CI/CD) for ML models: Functionally, this involves creating and managing the automated pipelines for machine learning workflows, like training, testing, and deploying. The desired outcome is to accelerate the ML lifecycle, ensure the continuous availability of updated and optimized models, and reduce manual errors.
- Production monitoring of ML models: The MLOps engineer should ensure that models in production are closely monitored for performance decay and logged for audit purposes. Functionally, this helps maintain the quality of the service provided by the model. The desired outcome is to maintain the reliability and accuracy of deployed models, ensuring the continued value to the business operations.
- Workflow optimization: Functionally, this involves working with distributed systems to ensure that ML models can handle larger datasets and high loads in production. The desired outcome is to provide robust, scalable machine learning solutions that can cater to growing business needs, ensuring high availability and performance.
- Model versioning and rollback: The MLOps engineer needs to manage different versions of ML models and their associated data. Functionally, this enables efficient experimentation, debugging, and rollback to a previous version if necessary. The desired outcome is to maintain model reliability, reduce risk, and provide a rapid response to any issues with the current model version.
- Troubleshooting: Functionally, this ensures that ML models perform well in a production environment. The desired outcome is to minimize the risk of deploying faulty models, maintaining the integrity and reliability of AI-enabled services.
- Automation of ML workflows: MLOps engineers are responsible for the automation of ML workflows which functionally reduces the manual effort and potential errors. The desired outcome is to increase the speed and efficiency of machine learning operations and free up data scientists to focus more on model development and less on operations.
In this article we explored the overlaps and distinctions in the responsibilities of ML Engineers and MLOps Engineers.
We began by discussing some of the reasons these positions are often conflated, including comparable job titles and the use of similar technologies. However, ML Engineers focus on building, training and validating machine learning models, while MLOps Engineers concentrate primarily on testing, deploying and monitoring models in production environments.
There are responsibilities shared between ML Engineers and MLOps Engineers, like security and compliance. But other duties clearly fall under the domain of ML Engineers alone, such as designing models. Similarly, responsibilities such as pipeline maintenance, and CI/CD implementation are solely under the purview of MLOps Engineers.
While these roles are complementary, it is important to clarify how they differ so that aspiring ML engineers or MLOps engineers can choose the right path.
Authors: Jose Gabriel Islas Montero, Dmitry Kazhdan, Botty Dimanov
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