MLOps Engineer vs ML Engineer: Notable differences

Last updated by Nitin Grover on Aug 30, 2024 at 06:15 PM | Reading time: 7 minutes

What are the main differences between MLOps Engineer and ML Engineer roles in the ML adoption life cycle?As business enterprises around the world look to refine their AI/ML operations, there is a growing need to clearly define the responsibilities of every individual. In this context, the roles of MLOps and ML Engineers are often perceived to overlap.

‍In this blog, we will look at the responsibilities and skills of an MLOps Engineer vs ML Engineer

If you want to pursue a fulfilling career of ML Engineer or MLOps Engineer, consider opting for the MLOps Engineering course offered by Interview Kickstart. This course is prepared and taught by hiring managers and tech leads at top IT companies in the world.

You may also have a look at the video below to find out what it takes to clear a machine learning interview in one of the top five IT companies in the world.

‍MLOps vs ML Engineer: Responsibilities of ML Engineer in Model Development

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ML Engineers are responsible for the entire lifecycle of machine learning models, from their initial development, to refinement and whenever required, retraining them. Their job profile revolves around conceptualization of machine learning models, creating prototypes of these models and implementing them to address specific business problems.
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ML Engineers collaborate with data scientists to fine-tune and optimize the models as per specific business needs. They work closely with data scientists and other stakeholders to improve the performance of these models by identifying domain-specific challenges related to that model.

In addition, they explore various systems of methods that would aid in these improvement initiatives, and subsequently experiment with a lot of hyperparameters to implement those improvements.

ML Engineers perform various tasks throughout the machine learning pipeline. They start with fixing corrupted or incorrect records in datasets and transform raw data into a more effective set of inputs.

Subsequently, they select appropriate models based on the problem and dataset they are working upon, train these models using various methodologies, and fine-tune these models based on specific business needs.
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ML Engineers leverage their expertise in machine learning algorithms, and deep learning procedures to build efficient models. By utilizing their understanding of supervised, unsupervised, or decisions-based algorithms, they devise tailored solutions to make the models perform even better.

Also Learn: Top 10 Machine Learning Algorithms every engineer needs to know

MLOps vs ML Engineer: Responsibilities of MLOps Engineer in Model Development

The MLOPs Engineers provide necessary infrastructure and tools to ensure seamless deployment of machine learning models. They facilitate access to the required platforms, frameworks, and environments required for ML Engineers for efficient development and deployment of models
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The MLOPs Engineers collaborate closely with ML Engineers to ensure that the pipelines that are being designed are efficient and fully scalable. During this collaboration, the MLOps Engineers develop the framework for smooth deployment of these pipelines, create a plan to mitigate any challenges or minimize any disruptions that may arise in their deployment and scalability.

Responsibilities of ML Engineer in Model Deployment and Management

Although ML Engineers are not directly involved in the deployment and management of ML models, they coordinate closely with the MLOps engineers to ensure that:

The models are packaged in a format suitable for deployment and that they are compatible with the deployment environment.
Models are optimized to perform well in real-time and in batches depending on the application environment.
Close collaboration with MLOps Engineers to ensure seamless integration of the model into the overall deployment pipeline.
Rigorous testing of the deployed models is performed to ensure accuracy, robustness and scalability.‍

‍Responsibilities of MLOps Engineer in Model Deployment and Management‍

The MLOps Engineer sets-up the infrastructure required for the deployment of machine learning models which involves provisioning computing resources, setting up software stacks like PyTorch and Scikit-learn and deployment tools like Karpenter and Kubestalk.
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The MLOps Engineer automates the deployment process to ensure that it’s reliable, consistent and scalable. The deployment pipelines automate the deployment model, from its packaging, to configuration of the involved infrastructure, and from testing the model to its deployment into production.

While doing so, they also ensure that the deployed model integrates into the organization's Continuous Integration and Continuous Deployment (CI/CD) pipeline.
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The MLOps Engineer optimizes the deployed model for performance and scalability. They ensure that the model is able to handle varying workloads and scale resources based on actual demands of the machine learning environment.

This includes, optimization of model inference speed and resource usage for efficient use of computing resources.
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The MLOPs Engineer also tracks and monitors the health and performance of the deployed system including monitoring latency, throughput, and error rates, setting up alerts for real-time monitoring of issues, retraining them with new data, and managing various model versions.

The MLOPs Engineer sets up best security practices such as access control and encryption, to prevent data leakage and unauthorized access. They also ensure that the deployments adhere to the regulatory requirements such as GDPR or HIPAA.

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MLOps vs ML Engineer: Skill Sets

Just like their areas of expertise, a few skills of ML Engineers and MLOps Engineers also overlap. Here we have highlighted a few common and a few unique skills of ML Engineers vs. MLOps Engineers.

‍‍‍ML Engineer Skills

Basic understanding of statistics and probability
Proficiency in building and evaluation of highly accurate machine learning models, including decision trees, clustering, and regression algorithms
Sound understanding of DevOps and its two key practices–Continuous Integration (CI) and Continuous Deployment (CD), as well as cloud platforms like AWS, Azure and Google Cloud
Practical knowledge of any one of the programming languages–Java, Python or C++
Excellent communication and problem-solving skills.

ML Engineer Salary‍

Average salary for an ML Engineer in the U.S. is $1,65,503 per annum.
Additional average cash compensation for MLOps Engineer in the US is $37,106 per annum‍

‍MLOps Engineer Skills

Proficiency in machine learning algorithms, machine learning models and statistics
Knowledge of processes such as data preprocessing, feature engineering and model evaluation
Experience in AWS, Azure and Google Cloud platforms
Practical knowledge of any one of the programming languages–Python, R, or Julia
Experience in deployment and management of machine learning models in cloud environments
Experience in containerization tools and container orchestration tools like Kubernetes Docker
Excellent communication and problem-solving skills

‍MLOps Engineer Salary

Average salary for MLOps Engineer in the U.S. is $1,76,839 per annum
Additional average cash compensation for MLOps Engineer in the US is $38,873 per annum

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Having done a fair research in the skills and responsibilities of MLOps Engineer vs ML Engineer, now lets tabulate the key differences in their areas of expertise and skills.

While an MLOps Engineer may have distinct responsibilities in comparison to an ML Engineer, in many organizations their responsibilities overlap with more than one ML Engineer performing the duties of both ML and MLOps job roles. However, by understanding and leveraging the unique strengths of each role, businesses can realize the true capabilities of machine learning to drive innovation and growth.

FAQs: MLOps Engineer vs ML Engineer

Which role is better in MLOps Engineer and ML Engineer?

Quite a few enterprises define clear and precise roles for MLOps engineer and ML Engineer. It’s very difficult to rate one role as better than the other with both making significant contributions in various stages of a machine learning life cycle.

What’s the future prospects of MLOps and ML Engineer roles?

As organizations around the world continue to realize the potential of artificial intelligence and machine learning, the demand for skilled professionals in these domains will continue to grow. So the future is bright for MLOps and ML Engineer job roles.

What is the difference between an AI engineer and an ML engineer?

An AI engineer uses techniques like machine learning, deep learning, natural language processing and computer vision to solve complex real world problems. ML engineers specifically work on development, deployment and self-tuning of ML algorithms and models.

Is MLOps a data engineer?

In some organizations data engineers can play a collaborative role of MLOps engineer along with devops engineers. However, in many organizations their role is clearly defined and is distinct from a data engineer in scope and responsibilities.

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AUTHOR

Nitin Grover

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Contents

Nitin Grover

Attend our Free Webinar on How to Nail Your Next Technical Interview

How to Nail your next Technical Interview

Worried About Failing Tech Interviews?

Tools to Enhance Full Stack Development with AI

The Future of Data Science: Emerging Trends and Opportunities

Data-Driven Decision Making: Your Roadmap to Business Success

The Business Impact of Machine Learning: Real-world Case Studies

What is OpenAI? Everything You Need to Know

Mock Interviews for Generative AI: Essential Practice to Land Top AI Roles

Top Python Scripting Interview Questions and Answers You Should Practice

Complex SQL Interview Questions for Interview Preparation

Zoox Software Engineer Interview Questions to Crack Your Tech Interview

Rubrik Interview Questions for Software Engineers

Top Advanced SQL Interview Questions and Answers

Twilio Interview Questions

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