MLOps vs. DevOps: Key Differences and Similarities

The speed of innovation has become inextricably linked to a company's ability to deliver dependable software and intelligent systems. That is the modern market for you. Anyway, for more than a decade, DevOps has served as the foundational methodology. It has brought together software development and IT operations teams to achieve continuous integration and continuous deployment. But with the rise of AI and ML, a new level of complexity has been created. The kind that traditional DevOps practices cannot fully address. ML models are not like standard application code; their performance is inextricably linked to the quality and evolution of the data they consume. Hence, to bridge the gap between the experimental nature of data science and the rigid demands of production IT, MLOps (Machine Learning Operations) was developed.

In this blog, I will discuss some of the most important differences and similarities between MLOps and DevOps. Then you will be able to make an informed choice between hiring a service provider for DevOps services and solutions and one for MLOps.

What Refers to as MLOps?

It is a collection of practices that brings together ML, DevOps, and data engineering. The aim here is to automate the entire lifecycle of an ML model. MLOps' primary goal is to reliably and efficiently move experimental ML models from the research environment to production.

DevOps: A Quick Lowdown

This is a methodology and cultural philosophy for software development (Dev) and information technology operations (Ops). DevOps seeks to accelerate and automate the entire software delivery process.

DevOps vs MLOps: Notable Differences

DevOps emphasizes software code delivery and infrastructure automation, while MLOps focuses on managing machine learning models, data quality, and continuous training. MLOps requires broader collaboration and lifecycle management beyond traditional CI/CD pipelines.

Listed below are some of the core differences;

• Focus: The former focuses primarily on the application lifecycle and code delivery. It guarantees dependable and automated delivery of static software apps. The primary artifacts managed are source code and application binaries among other things. Whereas MLOps puts the spotlight on the ML model lifecycle and data quality. It manages the model's continuous flow from experimentation to production and back. The deployed system's behavior is inherently non deterministic, as its performance is influenced by the dynamic and ever-changing real-world data it uses.
• Collaboration: DevOps drives this practice between two key groups: software developers and IT operations engineers. Developers write and test application code, whereas operations engineers work on infrastructure, system monitoring, etc. MLOps goes about it a little differently, requiring collaboration among a cross functional team. Owing to continuous data validation and model monitoring demands, better integration and communication between all specialized roles are a must.
• Scope: A DevOps pipeline focuses on continuous integration and continuous deployment. CI involves frequently integrating and testing code changes, whereas CD involves automating the software artifact's deployment to production. On the other hand, the scope of MLOps in this context is much broader. It expands the CI/CD pipeline to include Continuous Training (CT). The MLOps lifecycle encompasses not only code and deployment automation, but also automated data validation, model training, and more.

DevOps and MLOps: Similarities You Mustn't Ignore

Both DevOps and MLOps emphasize collaboration, automation, and continuous improvement. They break silos, encourage experimentation, and rely on version control to ensure efficient workflows, faster iterations, and reliable deployments across software and machine learning environments.

Here are some of the similarities that you must consider;

• Need for collaboration: Both methodologies require breaking down silos and establishing shared responsibility. DevOps refers to the close collaboration between developers and operations engineers to ensure stable releases. MLOps broadens this to include a larger, cross functional team, with data scientists and engineers added to the mix.
• Encouragement of experimentation: DevOps as well as MLOps encourages structured experimentation to fuel innovation. DevOps facilitates this by allowing for quick, automated releases of minor changes to gather feedback quickly. MLOps applies this to the model development phase, requiring strong version control for code and models. This allows data scientists to quickly test and iterate on various algorithms and features in production like environments.

Final Words

No matter if you decide to opt for DevOps services and solutions or those for MLOps, remember to carefully evaluate each option against your business' unique needs.