MLOps Engineer: Salaries, Stack and Roles
Machine Learning Operations (MLOps) is a concoction of processes, pioneering best practices, and underlying technologies that provide businesses a suitable and efficient means to deploy, scale, automate, and manage ML applications. The technology cuts across the entire machine learning application lifecycle, right from data collection all the way to model management.
Today, machine learning applications have become a key component in organizations, with more and more businesses looking for systematic ways to automate and implement their ML models. True to this, a 2021 report by Deloitte shows that the MLOps solutions market is expected to hit the $4 billion mark by 2025, which is mind-boggling considering the technology is relatively new.
Naturally, this growth has triggered a spiking demand in MLOps managed services. In 2020, for example, the demand for artificial intelligence and machine learning-related roles grew by up to 74%. Likewise, people from different backgrounds have been festering to break into the data industry, with most attempting to land a job as a data scientist, machine learning engineer, or DevOps engineer.
And talking about a data scientist, ML engineer, and DevOps engineer, what’s the difference? Below, we are going to pit MLOps vs ML engineer roles against each other to establish what differentiates them.
Data Scientist vs. DevOps Engineer vs. Mlops Engineer
Before we get ahead of ourselves, it is important to mention one thing; the entire lifecycle of a machine learning system is tightly intertwined and highly iterative. Production Machine Learning is hard and calls for DevOps, Data Engineering, and Data Science expertise.
If we were to illustrate the relationship between MLOps vs DevOps vs Data engineers who inhabit this growing space, this is how it would look like:
And now, let’s break down these three primary roles within the ecosystem to see what they entail.
Data Scientist Roles and Responsibilities
Right off the gate, a data scientist can simply be described as a business-focused engineer who studies, pinpoints, and solves problems within Machine Learning algorithms. If a company is looking to hire remote data scientists, they simply want individuals capable of streamlining ML algorithms and how they work as solutions, as opposed to dealing with object-oriented programming code solutions.
In terms of skills, a data scientist requires both technical and non-technical skills. They include;
- Statistical computing and analysis
- Machine Learning
- Deep Learning
- Processing huge data sets
- Data Visualization
- Data Wrangling
- Big Data
- Strong communication skills
DevOps Engineer Roles and Responsibilities
A DevOps engineer is an individual or individuals tasked with introducing tools, processes, and methodologies throughout the entire software development life cycle. Whether it’s coding, deployment, maintenance, or even updating an ML application, they help reduce the complexities between these tasks, making it easy to implement changes in an application.
Development teams can have different skills, goals, or even ideas to introduce new features or functionality to an application. Operation teams, on the other hand, could be leaning more towards preserving the stability of a machine learning application once deployed.
DevOps encompasses the unification and automation of this entire cycle, and DevOps engineers are key in combining code, application management, and application maintenance. And all of these tasks are pegged on the individual’s understanding of ML application development life cycles, DevOps culture, tools, philosophy, and practices.
MLOps Engineer Roles and Responsibilities
The MLOps engineer roles and responsibilities intersect between a data scientist and DevOps in the machine learning domain. Basically, a machine learning operations engineer oversees ML model production, testing, and code optimization to eliminate low latency. Along with that, they also ensure that MLOps solutions are scalable and compatible with tools like Kubernetes and Docker if needed.
ML models also process large amounts of data at record-breaking speeds, and an MLOps engineer is tasked with ensuring that they can handle the incoming data.
In terms of MLOps engineer skills, these individuals need to have strong software programming skills, a strong command of MLOps architecture, ML automation technologies, and be well versed with MLOps engineering on AWS, Azure, and GCP cloud solutions.
Other technical skills required to become an MLOps developer include:
- Ability to develop MLOps pipelines
- Deep understanding of Linux
- Knowledge of frameworks such as Tensorflow, Keras, and PyTorch
- Experience in using leading MLOps frameworks like MLFlow, DataRobot, and Kubeflow
- Strong knowledge in scripting, and Git and Git workflows
Why Hire MLOPs Engineers?
Now that we have drawn a parallel between a machine learning engineer vs MLOps, let’s drill into the minute details to reveal why and when to employ MLOps.
As earlier indicated, machine learning helps individuals and enterprises alike deploy technological solutions that save time, streamline workflows, and unlock previously untapped revenue sources, just to mention a few.
If you are still on the fence on whether to leverage MLOps or not, here are three pointers or indicators that it’s time to hire an MLOps engineer;
- You are a new company frustrated with manually deploying, monitoring, and replacing models by using incompatible machine learning IT processes.
- Your company is attempting to create or improve machine learning processes.
- A mature company looking to update its ML models.
As to why you should consider hiring an MLOps engineer, there are multiple benefits that you or your company can reap by utilizing MLOps services. They include;
- Machine Learning model lifecycle management. A good MLOps engineer can help build, deploy, and manage ML models, ensuring the integrity of your business processes. Along with that, they can also help by providing a consistent and reliable means to migrate your machine learning models from the development environment to the production environment.
- ML model versioning and iteration. After deployment in a particular industry, ML models need to be constantly versioned and iterated. To accommodate any new and emerging requirements, they need to be modified using real-world data for further training. An MLOps engineer can help create a personalized ML model, notify users of the changes, and maintain the model’s history.
- ML model monitoring & management. The business environment and the challenges facing business are constantly changing, making it extremely challenging to operate in a business environment with limited data. With the help of MLOps engineers, however, it becomes easier to continuously monitor and manage the usage and consumption of your model, making the results much more accurate.
- ML model governance. Real-world machine learning models need to be accurate and trustworthy. Any competent MLOps engineer should have proficiency handling a wide variety of governance-related tasks covering different areas including model auditing, testing, validation, compliance, access control, and access logs control.
- ML model security enhancement and management. In an increasingly interconnected world, the threat of data breaches continues to grow by the day, with the average cost of a data breach reaching $4.24 million in 2021. In line with this, machine learning models need to be shielded against unauthorized access. With the provision of functionalities to protect these models being one of the primary roles of MLOps engineers, your model’s data is safeguarded against being corrupted, destroyed by, or inappropriately accessed by unauthorized personnel.
- ML model discovery. MLOps engineers can also help startups and multinational corporations by providing model catalogs for models produced and searchable model marketplace, making it easier to track the data origination, quality transparency, and significance of model generation.
Senior MLOps Engineer Resume Sample
Whether you’re an active or passive job seeker, applying for a senior MLOps engineer job role takes much more than curating a long list of MLOps engineer interview questions and reciting your answers. And although practicing to answer MLOps interview questions is important, you need to guide the recruiter by highlighting your skills as listed on the MLOps engineer job description.
Here’s a sample resume of a senior MLOps engineer:
MLOps Engineer Salary
MLOps Engineer is one of the most highly paid job positions in the world, and MLOps companies are willing to pay top-dollar to lay hands on the best talent. Whilst the overall salary depends on several factors like the company, skills, and geographical location, MLOps engineers receive relatively more compared to their peers in the industry.
Here is a quick comparison of the average monthly salary rates of MLOps across different countries.
*Ukrainian salaries are provided based on Mobilunity’s Recruitment Team research on the local job markets. All salaries are net and do not include the service fee (in case of hiring on a dedicated team model). The salaries are provided for comparison purposes and could be not entirely accurate. Contact us to know the exact cost of hiring a developer corresponding to the required parameters.
Hire MLOps Engineer with Mobilunity
Although there are many outstaffing hubs around the world, Ukraine has emerged as the most dominant destination for companies looking to hire tech professionals. Along with their technical skills, tech professionals in Ukrainian are proficient English speakers, hardworking, and highly self-motivated.
Mobilunity is one of the leading IT outstaffing companies in Ukraine, and we have been helping small, medium, and multinational conglomerates source, assess, and hire dedicated developers throughout the world. Our team of committed recruiters has in-depth knowledge of Ukraine’s tech talent market, and they’ve spent years developing networks, allowing them to accelerate the hiring process.