Is MLOps Essential for Every Data Scientist?

While deep expertise in MLOps or DevOps isn’t necessary for all data scientists, a good understanding of these areas will definitely boost your effectiveness and career growth

7 min readSep 2, 2024

To succeed as a modern data scientist, you need more than just technical expertise in data science. Recruiters often highlight that companies expect data scientists to understand how their work fits with operations, business, and marketing, and how their insights affect various stakeholders. The role of a data scientist can vary widely across companies and even within the same organization, encompassing a range of responsibilities and skills. It’s not enough to just know data science theory and techniques anymore. You also need proficiency in software engineering, system design, and operations. You’ll need to grasp how models move from Jupyter notebooks to production, write clean, scalable code, design effective data pipelines, and handle deployments across different platforms. This is where MLOps comes in — a combination of software engineering and DevOps practices related to data science. Mastering MLOps can significantly enhance your career perspectives and help you manage personal projects more effectively.

Let’s dive into these concepts and explore:

How modern applications integrate traditional software engineering with machine learning.
Why DevOps knowledge is crucial for data science.
What a machine learning project looks like in a production environment.

You need to understand both traditional software engineering and ML to effectively build modern applications

When considering the differences between software systems containing ML models and traditional software systems without ML integrations, a key observation is the following diagram.

Distinctions between software systems containing ML models and software systems without ML integrations

In traditional software development, you write code to process data and get specific results. With machine learning (ML), you provide data and desired outcomes to create the code, allowing the system to learn and make decisions on its own. This highlights the main difference: ML systems learn from data, while traditional software relies on explicit instructions. So it is easy to notice that ML systems not only face the usual software development challenges but also bring out unique problems related to learning and adaptation of the models. While it was once common to doubt if ML was necessary, recent achievements have shown that ML can deliver impressive results, making its use more widespread.

In short, applications often combine traditional programming with ML, creating systems that leverage both rule-based logic and adaptive learning. This integration means you need to understand both traditional software engineering and ML to effectively build modern applications.

DevOps knowledge matter in Data Science

In today’s data science field, having a grasp of software engineering and DevOps fundamentals is essential because these areas are closely linked. Combining machine learning models with software requires solid software design and implementation skills, focusing on both data and ML models. Developing proficiency in software engineering and operations is crucial for success in data science. Good coding practices help you produce production-ready code, and knowledge of MLOps aids in setting up environments and managing costs effectively. Additionally, enhancing your engineering skills can:

Save time on routine data preparation
Improve data modeling techniques
Strengthen testing skills to catch errors and handle special cases

While some data scientists might focus more on their specialized skills and less on software engineering, it’s important not to ignore solid engineering practices and modern technologies. Understanding the operational side can lead to:

Better communication and management of deployed models
More efficient development and monitoring of metrics
Better error handling

You don’t need to be a coding expert to start as a data scientist; basic coding skills and a willingness to learn are enough. Similarly, you don’t have to master all DevOps tools, but having a basic understanding is very helpful.

What does ML project look like in production

Model training is at the heart of the data science development lifecycle. However, creating a model is just the first step; deploying it into production, actively using it, and solving real-world problems is equally important.

As mentioned, many data scientists view model deployment as primarily a software engineering task because of the skills involved. This makes sense given the typical responsibilities of software engineers. However, tools like Docker, MLflow, Terraform, and Git have simplified the process.
Deploying machine learning models is a crucial step that connects theory with practice. Many models never reach production, missing their intended impact. It’s important for data scientists to see their work in action, as each deployment offers a chance to learn and improve. Successful deployment involves accepting feedback, analyzing results, and continually refining your approach. While each deployment process is unique, following best practices can lead to success.

Deploying a machine learning model means integrating a trained model into a real-world system or application to automate predictions or specific tasks.

In production, an ML model includes three main parts:

Data
Model
Code

And the typical ML workflow in real-life applications involves three key stages:

Data Engineering: This involves acquiring and preparing the data.
ML Model Engineering: This covers training the ML model and setting it up for use.
Code Engineering: This involves integrating the ML model into the final product.

ML model deployment requires understanding how to “package” the ML solution to ensure its successful integration with existing software systems and how to design its performance monitoring and improvements over time.

In general, the ML project lifecycle in production is defined as follows:

Figure adopted from “MLOps: Continuous delivery and automation pipelines in machine learning”

Looks scary, right?
We won’t insist it is not.
But the good news is that there are many useful resources to learn about MLOps but in short, here are three main stages of ML system development that are crucial for every project to be successful in production.

Model Development
Before deploying machine learning models, thorough testing and validation are crucial for accuracy and reliability. After building the model, optimize and test the code, then clean and refine it as needed. Organizations often use centralized systems for automated experiment tracking to streamline testing and ensure the model performs well in a live environment. This transparency helps teams collaborate effectively and refine the code.

Model Deployment
Deploying models involves setting up a virtual environment, installing necessary packages, and using tools like Docker for smooth integration. This setup avoids conflicts by ensuring an isolated environment with all required packages. Tools designed for machine learning deployment help efficiently integrate models into real-world applications.

Continuous Monitoring and Maintenance
Ongoing monitoring and maintenance are key for successful ML model deployment. It’s not just about ensuring the model works initially; continuous monitoring is needed to maintain long-term effectiveness. Your team should regularly monitor, optimize, and retrain models to address issues like data drift, inefficiencies, and bias.

Successfully deploying an ML model doesn’t need to be challenging if all necessary aspects are addressed before starting a project. This is crucial for any ML project and should be given high priority! This is called MLOps.

Summing Up

MLOps practices and tools streamline the development and deployment of machine learning models. They combine the best practices from software engineering and data science to create a smooth workflow throughout the entire ML pipeline. The “Ops” in MLOps comes from DevOps, which stands for Development and Operations. Operationalizing means deploying, monitoring, and maintaining something in a production environment.

Getting really good at MLOps and applying it practically is an important skill for the upcoming data-driven era. Data scientist with understanding the whole data science process well, will be valuable to any organization. This skill is just one important part of becoming a great data scientist, often called a “unicorn.”

In our DS training program, we’re creating a community of future skilled data scientists with this mindset.

K-Minds 2.0 - DataPoint Armenia

The program comes with fully funded scholarships which you will be automatically considered once you submit your…

datapoint.am

This article, and the upcoming ones is our way of taking a step forward in this effort.

Before you go…

Your engagement, including comments, sharing, and participation in our initiatives, is highly appreciated.
DataPoint Armenia is a community of data scientists aiming to establish a network of individuals dedicated to continual learning, collaborative efforts, and knowledge-sharing.

Our community engages in various interesting modern projects, with a primary focus on our current key project — the training program called
K-Minds. This program is designed to provide hands-on training using real corporate data in real working environments. It aims to develop a range of soft and hard skills, assist in interview preparation. As part of our approach, we’ve chosen to document a part of this program through Medium blogging, with specific objectives in mind:

Expanding our audience to attract new stakeholders.
Sharing insights and receiving feedback on our findings.
Making a portion of our training program accessible to everyone.

Today’s article marks just the beginning of our journey, introduces the discipline and provides a general overview of what constitutes the lifecycle of a machine learning model in production.