ML Engineer Vs MLOPS Engineer

ML Engineer Vs MLOPS Engineer
ML Engineer Vs MLOPS Engineer

The ML teams of today are quite specialized and multi-talented. They are made up of individuals from a wide range of professions, educational levels, and skill sets.

While there are many different positions required within typical ML teams, we will concentrate on the two that receive the most questions: the machine learning engineer and the MLOps Engineer. Despite sometimes being confused, these two roles are very distinct from one another.

In order to help readers understand what each of these responsibilities entails, we’ll examine the distinctions between them in this article.

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What is a ML Engineer?

Engineers in machine learning are the hands-on, creative thinkers. Building machine learning systems using their models and putting them into production, they assist the work of machine learning engineers and data scientists by bringing their theory to the real world.

Due to the practical, hands-on nature of this position, ML engineers frequently have backgrounds in software development and DevOps. As a result, they are knowledgeable about concepts like containers, tools like Docker, managing clusters across different compute clouds, and constructing reliable ML deployment pipelines that can help a model thrive in the real world.

Skill Needed To Be A ML Engineer?

A candidate for a position as a machine learning engineer must have the following abilities and credentials:

  • Knowledge of calculus, linear algebra, and Bayesian statistics, as well as advanced mathematics and statistics.
  • Degree in statistics, computer science, mathematics, or a similar subject.
  • A master’s degree in deep learning, artificial intelligence, or a similar field.
  • Strong analytical, problem-solving, and collaborative abilities.
  • Software engineering skills.
  • Understanding of data science.
  • Programming and coding languages including Python, Java, C++, C, R, and JavaScript.
  • Grasp of ML frameworks in practise.
  • Practical knowledge of ML packages and libraries.
  • Recognize data modeling, data structures, and software architecture.
  • A working knowledge of computer architecture.

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Roles And Responsibilities of an ML Engineer

The project, business, and sector will all have a significant impact on the duties of an ML engineer. The primary roles and responsibilities of this role have been compiled after searching through numerous job boards.

  • To look into, modify, and employ scientific prototypes and data analytics.
  • To create and develop strategies and practices for machine learning.
  • Analyzing test results statistically and improving models using the results.
  • To search online for readily available training datasets.
  • It is important to train and update models and ML systems as necessary.
  • To improve and broaden the current ML frameworks and libraries.
  • To create machine learning apps in accordance with client or user needs.
  • should research, test, and employ the appropriate ML tools and algorithms.
  • To evaluate the capacity of ML algorithms to solve issues and rank them in order of likelihood of success.
  • To more fully understand data through exploration and visualization, as well as to identify differences in data distribution that could have an impact on a model’s performance when used in real-world scenarios.

Salary of ML Engineer in India

Machine learning is in high demand, but employers look for people with the right skill sets. These engineers are always in high demand. According to Payscale, the average salary machine learning in India is roughly ₹686,281 per year, including bonuses and profit-sharing.

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What is an MLops Engineer?

The MLOps Engineer, sometimes referred to as a machine learning operations engineer, is an essential addition to the data science team. Consider switching to this role if you’re currently a Software Engineer who wants to work cross-functionally but specifically with Machine Learning algorithms, or if you’re a Data Scientist who knows how the algorithms operate but wants to concentrate more on Software Engineering, Data Engineering, and model deployment. You might be given a Data Science model produced by a Data Scientist for the first time when working as an MLOps Engineer.

Skill Needed To Be An MLops Engineer?

  • Strong programming abilities, practical knowledge of machine learning frameworks and libraries, experience working in agile environments, and the ability to execute machine learning solutions leveraging DevOps principles are in high demand.
  • This sector requires a blend of DevOps, data engineering, and machine learning techniques.
  • DevOps engineers should constantly interact with QA teams throughout the CI/CD cycle and be aware of the testing history. To comprehend how your code is tested and maintained, you must have a solid understanding of the framework/environments directed by QA.
  • An experienced MLOps engineer should be knowledgeable about data structures, data modeling, and database management systems because machine learning depends so heavily on data.
  • A subset of the DevOps discipline is MLOps. Knowing how to automate the entire DevOps pipeline, including infrastructure configurations and settings, app performance monitoring, is crucial.

Roles And Responsibilities of an MLops Engineer

The following duties fall under the purview of an MLOps Engineer:

  • Responsible for evaluating models and optimizing hyperparameters in such models. These engineers concentrate on modeling the onboarding, operational, and decommissioning workflows. the governance and version control models, Data archiving and version control observing the model and how it is evolving.
  • To measure and improve service quality, establish standards, metrics, and monitoring procedures.
  • Delivers the finest options and carries out proofs-of-concept for automated and effective model operations on a large scale.
  • Creates and maintains scalable MLOps frameworks to support model-specific client requirements.
  • They provide technical design help for RFPs as the sales team’s MLOps expert.

Salary of MLOps Engineer in India

You could make a good living as an MLOps engineer. If you’re seeking a new job, think about becoming an MLOps Engineer. The standard pay for this position in India is ₹10,48,920.


At Datamites Institute, your success is our first focus. We created this programme to provide you the skills you require to succeed in machine learning and MLops engineer.

The DataMites® MLOps – Course is demanding and challenging but also rewarding because it is built on our rigorous Bootcamp learning model. You’ll learn how to use the most advanced AI tools now on the market and how to employ complex algorithms to solve problems in your industry.