Home
Talent

Back-end Developers

Front-end Developers

Mobile Developers

Other Roles

Our Services
Our Process
For Freelancers
Blog
Apply as a FreelancerHire Now

What is MLOps? Exploring Best Practices and Differences from DevOps

Profile Picture of Matheus Jacques
Matheus Jacques
Senior Data Scientist
DevOps, AI and Machine Learning elements to describe how ML is combined with DevOps to create MLOps

Machine learning (ML) has evolved from an experimental novelty to a fundamental part of the software landscape. As ML adoption has grown, development and deployment processes have had to adapt—and continue to evolve—to meet the demands of this field.

ML models are built at first for experimentation, and as such, typically lack robust infrastructure for large-scale testing and deployment. Instead, teams often rely on numerous manual processes, which lead to unique challenges as these models are transitioned to production environments. The main challenges include scalability, latency, model monitoring, and “model drift,” in which models lose accuracy over time as real-world data changes. These challenges highlight the need for robust systems that can adapt to evolving data and maintain performance in real-world applications.

Table Of Contents

In some ways, the software development industry has experienced this problem before – and addressed it – through the adoption of DevOps, which standardized and automated traditional software build pipelines. The application of DevOps to modern ML projects, however, uncovered the need for a more robust, ML-specific set of processes. As ML models transition from research projects to mission-critical systems, a new term has emerged: MLOps.

In this article, we’ll explore what MLOps is, why it’s so important, and how it differs from traditional DevOps practices.

Where it Started: The Introduction of DevOps

Before the introduction of DevOps, development and IT teams were often at odds with one another. While developers typically focus on rapid innovation and frequent code changes, IT teams want to keep systems secure and reliable. These disparate priorities often lead to information silos, inefficient deployment cycles, and frustration on both sides.

Table comparing the fundamental differences in goals and priorities between development and IT teams
Development teams and IT teams often have fundamentally different goals due to their distinct functions within an organization

In an effort to bridge the gap between software developers and IT operations teams, Patrick Debois introduced the concept of DevOps in 2009. Through a set of business methodologies, tools, and practices, DevOps helps to facilitate a more collaborative and efficient software development lifecycle. 

In case you’re a little rusty, here’s a brief refresher on the key principles of DevOps:

Originally published on Sep 30, 2024Last updated on Oct 10, 2024
Key Takeaways

What is the difference between MLOps and DevOps?

MLOps and DevOps share core principles like automation, collaboration, and continuous improvement, but MLOps adapts these principles to address the unique challenges of deploying machine learning models. While DevOps focuses on managing code and delivering software, MLOps deals with the complexities of managing data, models, and code. MLOps requires specialized tools for data and model versioning, must handle non-deterministic testing for fairness and robustness, and focuses on monitoring data and model drift to ensure accuracy over time.

What is MLOps in simple terms?

MLOps, or Machine Learning Operations, is a set of practices that helps manage and automate the end-to-end process of developing, deploying, and maintaining machine learning models. It combines machine learning with concepts from DevOps to ensure that ML models are reliable, scalable, and consistently deliver good results in production. Essentially, MLOps makes it easier to turn experimental ML models into practical, operational tools.

What is MLOps used for?

MLOps is used to manage and streamline the lifecycle of machine learning models, from development to deployment and ongoing maintenance. It ensures that ML models are consistently trained, tested, deployed, and monitored efficiently. MLOps helps automate data preprocessing, model training, deployment, and performance tracking, making it easier to keep models updated, handle changing data, and maintain their accuracy in real-world applications.

Looking to hire?

Join our newsletter

Join thousands of subscribers already getting our original articles about software design and development. You will not receive any spam, just great content once a month.