How to Become an MLOps Engineer in 2024?

How to Become an MLOps Engineer- A Complete Roadmap to Getting Started with Machine Learning in Production

How to Become an MLOps Engineer in 2024?
 |  BY ProjectPro

In the past few years, there has been a massive increase in the demand for data-related roles. The hiring for machine learning and artificial intelligence-related roles has grown by 74% annually.  People from a multitude of backgrounds are trying to break into the data industry. Most of these individuals attempt to land a job in data science or analytics. However, there are many lesser-known career options in the data industry. This article will walk you through the job scope of a relatively new data-related career — an MLOps engineer.

MLOps sits at the intersection of data science, DevOps, and data engineering. An MLOps engineer brings machine learning models from test to production using software engineering and data science skills.


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In this article, we will cover the following topics:

  • What is MLOps? What is the role of an MLOps engineer?

  • The demand for MLOps engineers in the industry

  • Difference between an MLOps engineer, Data Scientist, and DevOps engineer

  • Skills Required to Become an MLOps engineer 

  • How you can become an MLOps engineer (A Complete Roadmap)

 

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MLOps Engineers - The Demand and Hype

MLOps topped LinkedIn’s Emerging Jobs ranking, with a recorded growth of 9.8 times in five years.

Most individuals looking to enter the data industry possess machine learning skills. However, most data scientists are unable to put the models they build into production.  As a result, companies are now starting to see a gap between models and production. Most machine learning models built in these companies are not usable, as they do not reach the end-user’s hands. MLOps engineering is a new role that bridges this gap and allows companies to productionize their data science models to get value out of them.

This is a rapidly growing field, as more companies are starting to realize that data scientists alone aren’t sufficient to get value out of machine learning models. It doesn’t matter how highly accurate a machine learning model is if it is unusable in a production setting.

Most people looking to break into the data industry tend to focus on data science. It is a good idea to shift your focus to MLOps since it is an equally high-paying field that isn’t highly saturated yet. 

Why should you become an MLOps engineer?

The demand for talent in all data-related careers is at an all-time high. 

Data science is a highly competitive field. It is very hyped up, and most fresh graduates are looking to land a data science job these days. MLOps is not as popular as data science, but the field offers a similar pay scale. The average MLOps engineer salary in the US is approximately $100K, while a data scientist earns around $119K on average.

Companies are looking to hire more people who can put machine learning models into production, so many opportunities exist in the MLOps domain.

Since not many people possess a skillset in the intersection of machine learning and software development, companies face a shortage of MLOps engineers. This is a great time to learn the skills required and apply for a job in MLOps, as career growth will be rapid since this industry hasn’t been saturated yet.

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Who is an MLOps Engineer?

As an MLOps engineer, you will need to deploy machine learning models and ensure they are functional in production. You don’t need to build the models yourself, so machine learning skills alone aren’t sufficient for this role.

However, you still need to understand the underlying machine learning algorithm to be able to put the model into production.

The data science team will build the machine learning model, but you might need to tweak some of their codes for deployment. Most models built by data science teams aren’t feasible for production since they can’t handle large amounts of data that enters the system in real-time. As an MLOps engineer, you will need to integrate the machine learning model into the company’s existing data infrastructure. You also need to work on optimization so that the model can handle large amounts of data in a production environment.

Production systems need to handle an endless amount of data entering the server daily. As traffic enters the system, the model needs to scale to come up with predictions efficiently. The MLOps engineer also might need to tweak the model and incorporate improvements from time to time without impacting system performance.

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A Day in the Life of An MLOps Engineer

The data science team builds a model that makes predictions on traffic entering the company’s server. Traffic that is classified as malicious by the ML model needs to be blocked from accessing the server. 

After the data science team builds the machine learning algorithm, the MLOps engineer needs to deploy this model on the company’s server. Traffic that is predicted as benign will automatically be allowed to access the system, while malicious traffic needs to be blocked. An alternate landing page will be shown to each individual.

All this needs to be done in real-time, and the predictions need to be made quickly to minimize latency. To do this, the MLOps engineer needs to optimize the codes written by the data science team.

As an MLOps engineer, you will use software engineering and DevOps skills to operationalize AI and ML models.

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Data Scientist vs. DevOps Engineer vs. MLOps Engineer

The skills of an MLOps engineer intersect with that of a data scientist and a DevOps engineer. We will now describe the difference between these three different career titles so you get a better understanding of them:

Data Scientist

If you’re reading this article, you likely already have a fair understanding of a data scientist’s job scope. A data scientist munges large amounts of data to build machine learning models and glean valuable insights. Most data scientists either use Python or R to build models. Libraries like Keras, Tensorflow, and Pytorch are popular amongst data scientists, and this may vary depending on the task at hand.

Data scientists have a strong understanding of statistics, machine learning algorithms, and programming. They understand the models to be built based on different use cases and can create models that can solve a company’s problems. However, data scientists don’t possess the skill set of software developers. They cannot put their models into production, and that is where the role of an MLOps engineer comes in.

DevOps Engineer

A DevOps engineer deploys and maintains applications, managing all aspects of its infrastructure. After software engineers build the application, the DevOps engineer handles deployment, cloud monitoring, database management, and testing.

This individual builds and tests code, deploys the application, manages error logging, and monitors the application continuously. They also need to ensure that the application is scalable and can handle heavy traffic without slowing down or breaking.

A DevOps engineer needs to have a strong understanding of software development, as they need to manage the entire application flow from code to production.

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MLOps Engineer

MLOps lies at an intersection of the above careers. An MLOps engineer essentially does the job of a DevOps engineer in the domain of machine learning. An MLOps engineer is in charge of everything that happens once the machine learning model is built. 

They put the model into production, test it to ensure it is working correctly, and optimize code for low latency. They also make sure that the machine learning application is scalable and use tools like Docker and Kubernetes if required.

Large amounts of data need to be processed quickly, and an MLOps engineer needs to ensure that the application can handle the entering data.

These individuals need to have strong programming and software engineering skills. They should understand model deployment on cloud platforms like GCP and have a strong command of automation technologies. Familiarity with more than one programming/scripting language is an added advantage.

An MLOps engineer needs to have a basic understanding of the machine learning frameworks used in the application and a solid knowledge of software development.

MLOps Engineer Skills

As mentioned above, an MLOps engineer needs to have an understanding of machine learning and software development. Here are some of the technical skills required to become an MLOps engineer:

  • Ability to design and implement cloud solutions (AWS, Azure, or GCP)

  • Experience with Docker and Kubernetes

  • Ability to build MLOps pipelines

  • Good understanding of Linux

  • Knowledge of frameworks such as Keras, PyTorch, Tensorflow

  • Experience with software development 

  • Ability to understand tools used by data scientists

  • Experience in using popular MLOps frameworks like Kubeflow, MLFlow, and DataRobot

Here are some non-technical skills required to become an MLOps engineer:

  • Strong communication skills — you need to be able to communicate with the data science team to understand the frameworks and types of models built

  • Teamwork — As an MLOps engineer, your team would have people from many different backgrounds. Some of them might have more data science knowledge, while some might come from a software development background with little machine learning knowledge. You need to work with individuals with diverse skillsets and play on their strengths to develop a scalable application.

Educational Background Required to Become an MLOps Engineer

MLOps engineers require a skill set that comes from multiple different fields. They need to understand data science and machine learning algorithms while also possessing some software development skills. Most MLOps engineer job listings indicate that they prefer candidates with a quantitative degree in any of these fields:

  • Computer Science

  • Engineering

  • Computational Statistics

  • Data Science

  • Mathematics

However, most employers understand that MLOps is a constantly changing field, and the most extensive qualification a candidate needs to possess is the ability to learn new tools. One can quickly gain software development and data science knowledge outside a degree. Companies are willing to hire individuals who don’t hold the preferred qualifications but can still do the job.

You can teach yourself the skills required to become an MLOps engineer, and we will provide you with a learning path to become an MLOps Engineer in the next section.

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How to Become an MLOps Engineer?

To become an MLOps engineer, you will need to learn the following data science and DevOps skills:

Kow-How of A Programming Language

You can start with Python since that is the language used by data scientists at large. It is an added advantage if you learn languages like C++ because of their faster runtime and rich machine learning library support.

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Managing Servers

You need to know how servers work to become an MLOps engineer. You should also learn about different operating systems, especially Linux. If you don’t have a Linux OS, you can download a virtual machine like Ubuntu to get started.

Learn Scripting

You need to learn a scripting language to automate processes as an MLOps engineer. Bash is one of the most popular scripting languages today, so you can start with that. Python, Go, and Ruby are popular scripting languages used to drive automation when deploying machine learning applications.

Model Deployment

You will need to deploy machine learning applications to a production server as an MLOps engineer. Create basic Python ‘Hello World’ applications and practice deploying them.

Most large companies use cloud platforms to host their machine learning applications. AWS, GCP, and Microsoft Azure are three of the most popular cloud platforms today, and experience with these cloud platforms is mandatory for most MLOps job listings.

Pick one of these platforms and learn to deploy and scale models on them.

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Machine Learning Algorithms and Models

As an MLOps engineer, you need to understand the models you are working with. You need to learn about the frameworks used to build these models and have a basic understanding of the underlying machine learning algorithm.

To do this, you should start learning frameworks. Try building supervised and unsupervised learning models with frameworks like Scikit-Learn in Python. Then, move on to deep learning frameworks like Keras, Tensorflow, and Pytorch. Learn the basic types of neural networks and their applications (e.g., CNNs are primarily used for computer vision, and RNNs are used for sequence prediction). Once you learn the basics of these models and their frameworks, you will be prepared for the data science aspect of the job.

Databases

An MLOps engineer needs to know how to work with databases. They need to create a database that can collect and store external data in real-time. The output of the machine learning models might also need to be stored for logging purposes, and the MLOps engineer needs to manage and scale the database system. The types of databases you use might differ based on your company, so make sure to learn to work with both SQL and NoSQL databases.

Here’s the Truth About Making Transition into MLOps

Even before reading this article, you might have heard people say that “only a small portion of machine learning models make it to production.

This is because many data science teams cannot deploy and scale their models. Their work usually ends at model building, and most are unaware of bringing models into a production environment.  Here come MLOps Engineers to rescue. As an MLOps engineer, you will make machine learning models available to the end-user. You will take care of everything that comes after the machine learning model is built, including testing, logging, deployment, and scaling. You need to possess the skill set of a DevOps engineer and a basic understanding of machine learning frameworks.

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Data Science Interview Preparation

Most MLOps engineers come from a software development background rather than a data science background. It is a highly technical job that requires knowledge of scripting, managing servers, and application layers.

However, if you come from a data science or statistics background, it is still possible to transition into MLOps with the right resources. 

If you come from a development background and aim to become a data scientist, it might be a good idea for you to take on an MLOps role first. You will get acquainted with machine learning models and work very closely with the data science team making the transition into data science more accessible.

If you don’t come from a technical background and are contemplating applying for a job in MLOps, it is possible. There are many successful self-taught MLOps engineers in the industry today. You just need to learn some of the programming languages and data science skills listed above.  Working on some hands-on MLOps projects to learn how to deploy machine learning models in production can be a great start to becoming and MLOps engineer. Getting certified as a cloud practitioner (on platforms such as AWS, GCP, or Microsoft Azure) is also an excellent qualification on your MLOps engineer resume if you don’t possess a technical degree.

 

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