Machine Learning Operations vs. DevOps
If you're familiar with programming and software development, you might have heard two terminologies that are often used interchangeably but are fundamentally different from each other: Machine Learning Operations and DevOps.
Both of them exist to improve the efficiency of software development, but they differ in how they approach it. In this blog post, we'll compare both Machine Learning Operations (MLOps) and DevOps, explore how they differ, what their advantages and disadvantages are, and ultimately, help you decide which one you should use.
What is DevOps?
In simplest terms, DevOps (short for Development and Operations) is a set of software development practices that aim to shorten the system development lifecycle and provide continuous delivery of high-quality software.
DevOps engineers coordinate with both software developers and operations teams to improve collaboration and automate the software delivery process. They aim to break down silos between teams and remove bottlenecks, leading to faster innovation and releases.
What is Machine Learning Operations?
MLOps is one of the newest trending methodologies of software development created to bring Machine Learning (ML) to the world of software development practices. MLOps refers to the application of DevOps practices in machine learning workflows to provide automation, monitoring, and management capabilities for ML models.
Machine learning models require developers to write and test model code, data engineers to develop data pipelines, ML engineers to select algorithms and architectures, production teams to deploy models, and monitoring teams to detect and diagnose model performance. MLOps brings together all of these teams to ensure that the entire machine learning architecture is operated and maintained smoothly.
How are DevOps and MLOps Different?
At their core, MLOps and DevOps share many similarities. Both methodologies ensure that software development processes are more efficient, automated, and collaborative across development teams.
However, where they differ is in their target domains. DevOps is more focused on software delivery and infrastructure management, while MLOps is aimed at creating, training, deploying, and maintaining machine learning models.
In simple words, DevOps is more for software development, while MLOps is more for software development that involves machine learning algorithms.
Which One is More Efficient and Effective?
Comparing DevOps and MLOps solely on the efficiency and effectiveness of their processes can be challenging. Both processes are based on collaboration between different teams, agile practices, automation, and continuous delivery.
However, research indicates that expanding automation capabilities with MLOps practices is critical to delivering more efficient and cost-effective machine learning applications (1). With the added advantage of integrating ML models with DevOps pipelines, MLOps is efficient in delivering better data-driven business insights.
In conclusion, software development methodologies like DevOps and MLOps have their advantages and disadvantages. It's crucial to weigh your project requirements and team capabilities before choosing which framework to implement.
But if your software development project involves machine learning models, MLOps can reduce manual intervention errors in the ML workflow, create reproducible workflows, and enhance team collaboration, ensuring efficient and error-free software delivery.
References
- MLOps – Machine Learning Operations by Wikipedia.