MLOps vs DevOps: What are the differences?

Artificial intelligence (AI) and machine learning (ML) have been making waves in recent years, transforming industries and revolutionizing the way we live and work. From virtual assistants to self-driving cars, AI has become an integral part of our daily lives. But as AI continues to advance, the need for efficient and effective deployment of ML models has become increasingly important.

In response to this growing demand, two distinct disciplines have emerged: MLOps (Machine Learning Operations) and DevOps. While both MLOps and DevOps share similar goals and principles, they differ in their focus and approach. In this article, we'll explore the differences and similarities between MLOps and DevOps, and discuss how they can work together to improve the deployment and management of ML models.

What is DevOps?

DevOps is a set of practices that combines software development (Dev) and IT operations (Ops) to improve the speed, quality, and reliability of software releases. DevOps aims to bridge the gap between development and operations teams by promoting collaboration, automation, and continuous integration and delivery (CI/CD).

For example, in most companies, a DevOps engineer is responsible for the development, deployment, and monitoring of their own app. This means that the DevOps engineer is not only responsible for writing the code for the app, but also for ensuring that it is properly deployed to the production environment and that it is running smoothly. This approach allows for faster and more reliable software releases, as well as improved communication and collaboration between development and operations teams.

In a DevOps environment, the traditional silos between development and operations teams are broken down, and teams work together to ensure that software is delivered quickly and reliably. This approach requires a cultural shift, as well as the adoption of new tools and practices.

The ideal DevOps lifecycle

In a DevOps environment, the traditional silos between development and operations teams are broken down, and teams work together to ensure that software is delivered quickly and reliably. The ideal DevOps lifecycle includes the following stages:

1. Plan: Define the project scope, goals, and requirements.
2. Code: Write high-quality code that is modular, testable, and maintainable.
3. Build: Compile and package the code into a deployable format.
4. Test: Verify that the code works as expected through automated and manual testing.
5. Deploy: Release the code to production, either manually or automatically.
6. Operate: Ensure the smooth operation of the application in production, including tasks such as scaling, patching, and troubleshooting.
7. Monitor: Track the performance and health of the application in production with website & performance monitoring and infrastructure monitoring.

What is MLOps?

MLOps is a discipline that applies DevOps principles to the deployment and management of ML models. MLOps aims to bridge the gap between data science and operations teams by promoting collaboration, automation, and continuous integration and delivery of ML models.

Key differences between MLOps and DevOps

While both MLOps and DevOps share similar goals, there are key differences between the two disciplines:

• Data scientists: In MLOps, data scientists play a crucial role in developing and deploying ML models. However, in DevOps, the focus is on software development and deployment, and data scientists are not always part of the team.
• Deploying models vs. code: DevOps is all about deploying code, whereas MLOps is about deploying ML models. This means that MLOps requires a different set of tools and techniques, such as model serving, model monitoring, and model explainability.
• Data quality matters: In MLOps, data is king. ML models require high-quality and diverse data to train and deploy, whereas in DevOps, the focus is on code quality and availability.
• The need for interpretability: MLOps requires model interpretability and explainability to ensure that machine learning models are transparent and fair. This is not always the case in DevOps, where the focus is on code readability and maintainability.

By highlighting these differences, we can see that MLOps and DevOps are not just different disciplines, but they also require different mindsets and approaches. While DevOps is all about deploying code, MLOps is about deploying ML models, and this requires a different set of skills and expertise.

In a nutshell

MLOps and DevOps are two distinct disciplines that share some commonalities, but also have key differences. MLOps focuses on deploying and managing machine learning models, while DevOps focuses on software development and deployment. Understanding these differences is crucial for teams to work together effectively and improve the deployment and management of machine learning models. And don't forget: they are not just buzzwords.