DevOps vs. MLOps: Differences & Similarities

Development teams face never-ending pressures to deliver new software at a fast and furious rate. Because of these demands, machine learning has become more adopted within development lifecycles to add and improve automation. 

We’ve been witnessing the use of machine learning and AI within software development for a while, including in the evolution of the CICD pipeline, and the testing tools being made smarter with ML. 

The latest quest at hand involving machine learning and AI is now determining how to properly implement successful machine learning projects and confidently push them to production in the same vein that DevOps does with software development. Enter MLOps.

MLOps is a specialized subset of DevOps for machine learning projects and applications. One way to understand how the two align is comparing them to some popular Winter Olympic sports. 

For anyone who has watched the Winter Olympics, the variety of competitive cold weather sports has evolved over the decades. The Olympics continue to expand competitions, introducing us to additional sport variations that are often similar-but-different than ones we’re already familiar with. 

If you think of DevOps as Nordic skiing, then consider MLOps as Nordic Combined, which includes both Nordic cross-country skiing AND ski jumping. MLOps includes similar approaches as DevOps (think Nordic), but focuses on automating the development and deployment of machine learning projects (adding ski jumping). The Nordic Combined event starts with a large ski jump that goes into a nordic race. Think of this addition as a variation of the continuous DevOps cycle that applies to machine learning development lifecycle.

This analogy might be a jump, so let’s take a refresh on DevOps and how it differs from the emerging MLOps, as well as where they intersect.

What is DevOps?

MLOps is a specialized subset of DevOps for machine learning projects and applications. The goal of DevOps is to decrease the development life cycle and to provide continuous delivery of high quality software products and applications. 

What is the DevOps lifecycle?

The DevOps lifecycle consists of a combination of five key continuous components: software development, integration, testing, deployment, and monitoring. 

The aim of the DevOps life cycle is to build the highest quality software. Each stage of the lifecycle is critical to success, and DevOps stages are performed on a loop until the product quality is achieved. 

For many developers, the DevOps life cycle can feel bottlenecked specifically in the testing phase. Increased execution time of tests bogs down the desired pace of code changes, particularly with large test suites. Launchable’s Predictive Test Selection amplifies testing cycle speeds by using machine learning models to create dynamic subsets of the most important tests for individual changes, and rapidly providing the feedback from the right tests. This is a great example of utilizing machine learning within DevOps, which is different from MLOps.

What is MLOps?

MLOps stands for Machine Learning Operations.  MLOps is a set of practices that works to efficiently and consistently deploy and maintain machine learning models. MLOps combines the automation of machine learning with the continuous development practices of DevOps. 

The end game of MLOps is to automate the deployment of ML models into software systems, eventually sans human intervention. You could also say the goal of MLOps is to help organizations proficiently run their AI and machine learning programs.

What is the MLOps lifecycle?

The MLOps lifecycle involves training, testing, packaging, validating, deploying, monitoring, and retraining the machine learning model as needed. This lifecycle advocates for automating and monitoring all steps of machine learning system construction, resulting in a culture that deploys best practices from DevOps for the machine learning environment.

DevOps vs MLOps

DevOps is often considered the precursor, or parent to MLOps, as MLOps is essentially modeled after DevOps. Both practices aim to create, deploy, test, and manage applications with more efficiency. Both approaches are being utilized within the modern software development cycle. And both approaches work to combine practices to enable teams to more rapidly develop, deploy, and scale.

However, despite their similarities, there are several key differences between DevOps and MLOps. These differences fall into four major categories: Development, Version control, Monitoring, and Team roles and responsibilities.

1. Development

For DevOps, development refers to the creation of code for new applications or features. Once written, code is deployed and validated against a series of tests. Typically, the development cycle processes can be automated with the help of machine learning.

However, development means something different in MLOps. In MLOps, development refers to the code that builds and trains a machine learning model. For deployment and validation, MLOps is only concerned with how well the ML model performs against various sets of test data.

In both DevOps and MLOps, development cycles are repeated until the desired quality or performance is finally achieved. However, DevOps development refers to the entire software product creation, while MLOps only refers to the machine learning models that may help speed the DevOps development cycle along the way.

2. Version Control

Version control is another area of key difference between DevOps and MLOps. In the broadest sense version control, or source control, refers to any tool or system that stores and tracks changes to projects.

DevOps focuses on tracking the changes to the code and to the artifacts. Version control helps DevOps teams to more quickly detect and correct errors, work simultaneously on the same project, and make it easier to find and communicate changes to code. For DevOps, version control helps developers and operations work faster together to rapidly release new features or applications.

Meanwhile, version control for MLOps revolves around tracking the changes made during the building and training of a machine learning model. This means MLOps teams track and measure the variables of each and every experimental run, including data, model building code, and the model artifact. Additionally, MLOps measures analytics on the model’s performance, for future tweaks and testing.

3. Monitoring

A third area of difference between DevOps and MLOps is the monitoring phase. For DevOps, monitoring is exhaustive and encompasses the oversight of the entire software development lifecycle. This includes planning, development, integration, testing, deployment, and operations.

On the other side of the coin, MLOps teams focus on monitoring the machine learning model. MLOps monitors potential issues within the machine learning model and seeks to eliminate data drift, feature importance, and model accuracy defects.

4. Roles and Responsibilities

DevOps and MLOps both share the common goals of increased communication and better workflow, but go about meeting this goal in two unique ways.

DevOps involves both developers and IT operations to speed up the development life cycle and offer continuous delivery of the highest quality software. DevOps also includes quality assurance, and as of recently security is becoming a more focused role within DevOps, with the trending field of DevSecOps. DevOps works to cut down the barriers between Development and Operations to enable more effective communication and enable speedier deliveries, while maintaining high quality.

Within MLOps the team roles and responsibilities include the ML team, or the data scientists, who alongside the Ops team, which focuses on operations and production. ML and Dev Operations differ; Both strive to deploy software into a fault tolerant, repeatable workflow, but with MLOps the software includes a machine learning component. 

DevOps and MLOps complement and differ in many ways, but MLOps can best be considered a specific implementation of DevOps that focuses on machine learning applications. By applying DevOps principles to machine learning systems, MLOps helps to maintain seamless integration between the development and deployment of ML models, making large-scale data projects possible.