What Is the Difference Between DevOps and MLOps

In 2022, we are well past the point of proving the value of machine learning; it is something that has taken the IT space by storm as a truly transformative innovation. As the technology has started to proliferate and become more commonplace, it has naturally been incorporated into more high-level projects, and the focus has shifted on how to properly implement successful machine learning projects and push them to production. 

Enter MLOps. For those of you who have worked on traditional software projects before, MLOps might sound familiar. This is because it is closely related to, but different from, DevOps. In this article, we are going to look at MLOps vs DevOps and how the two are different.

What is DevOps?

DevOps stands for development operations, and it is the combination of cultural philosophies, practices, and tools that increases an organization’s ability to deliver applications and services at scale and at speed. DevOps enables products to be evolved and improved at a faster pace than traditional software development and infrastructure management process. Ultimately, this speed enables organizations to better serve customers and compete more effectively in the market. 

Under a DevOps model, development and operations teams don’t exist in silos. It’s often the case that the two teams are merged into a single team where the engineers work across the entire application lifecycle, from development and test to deployment to operations. This leads to a shorter build-code-deploy loop, providing teams with the freedom to develop faster and produce a better end product. 

DevOps is ideally done with automation and uses tools like Jenkins and Git integrations. Based on key metrics and definitions of success, development teams then monitor the project to ensure it is meeting them. Just like building software, machine learning is highly experimental and follows similar concepts, and has its own discipline—MLOps. 

What is MLOps?

MLOps stands for ‘Machine Learning Model Operations’ and it is being touted as the best way for ML teams to eliminate many of the ML model development pain points experienced during the machine learning lifecycle and deliver more powerful ML models more easily.

MLOps is a core function of machine learning engineering that is focused on streamlining the process of deploying ML models into production and then monitoring and maintaining them once there. It is a collaborative movement that involves everyone from across the machine learning value chain – business leaders, data scientists, IT departments, and DevOps engineers – to achieve this goal.

Although MLOps is a relatively new discipline, it is quickly gaining traction as the best approach for the creation and deployment of high-quality ML models and AI solutions. By adopting an MLOps approach, ML teams can collaborate more easily and reduce model development and production times through the implementation of continuous integration and continuous deployment (CI/CD) practices that are supplemented with monitoring, validation, and governance.

There are many advantages that the existence of MLOps creates. 

• MLOps helps businesses to build efficient machine learning strategies by combining the knowledge of a business’s operational teams with those of its data science and engineering teams.

• MLOps automates model development and deployment, thus leading to benefits such as accelerated release times, lower operational costs, better decision making, and more agility. 

• MLOps places a business’s operational times at the forefront of the regulatory process. This is very important because the insights that are gained from data will have no relevance if they disregard best practices and compliance requirements.

• MLOps enables better collaboration between operational teams and data science and engineering teams, optimizing the division of labor. 

So, what’s the difference?

MLOps as a discipline takes many principles from DevOps and builds upon them. While the concepts of MLOps are still being developed, it is already possible to see similar foundational concepts being put into practice as they were in DevOps. 

Simply put, both DevOps and MLOps encourage and facilitate collaboration between those who develop (software engineers and data scientists), people who manage infrastructure, and other stakeholders. Both MLOps and DevOps emphasize process automation in continuous development so that speed and efficiency are maximized.

When it comes to testing, however, MLOps require additional methods on top of what has already been established for DevOps. This may be through steps such as data validation, model validation, or model quality testing. Depending on the type of machine learning model, teams may also need to set up pipelines for ongoing data handling and training. 

Comparing DevOps and MLOps 

Now that we have covered the underlying principles of both DevOps and MLOps, let’s take a look at the similarities and differences between the two. 

DevOps and MLOps similarities

First, both DevOps and MLOps are designed to streamline and automate processes. DevOps brings together the development, testing, and operational aspects of software development. In doing so, it aims to turn siloed processes into a set of continuous, cohesive steps. In a similar way, MLOps are the methods used to streamline the complicated machine learning lifecycle end-to-end, bridging the gap between design, model development, and operations to speed up model development.

On top of streamlining and automating processes, DevOps and MLOps also prioritize communication. In the case of DevOps, clear communication is required for the automation of processes, continuous delivery, and feedback loops. This is because DevOps relies on collaboration between different teams and departments, and a set of tools that facilitate these recesses in a visible way (for example, CI/CD systems). Meanwhile, in MLOps, communication is the basis for collaboration between system administrators, data science teams, and others, leading to a common understanding of how ML models are developed and maintained. 

DevOps and MLOps differences

Although there are plenty of similarities between the two, it is impossible to apply DevOps directly to machine learning. In the same vein, it is also impossible to apply MLOps directly to software development. Both disciplines have areas and requirements that are specific to their domains. In machine learning, these are:

‍Versioning—In DevOps, code version control is used to ensure clear documentation regarding any changes made to the software being developed. In machine learning, however, the code isn’t the only input that changes. Data is another critical input that regularly changes—typically more often than the code itself—and this requires careful management to eliminate potentially fatal problems such as training-serving skew and bias. 

Hardware—Training machine learning models, especially for deep learning applications, is a very compute-intensive process. Larger models might take hours or even weeks to train, meaning that an MLOps setup must be more sophisticated in nature. In contrast, the hardware on which software development is completed is usually irrelevant, and build times can be as low as a few minutes. 

Monitoring—Monitoring is an important part of DevOps just as it is in MLOps. The difference between monitoring in DevOps and monitoring in MLOps, however, is that while software doesn’t degrade over time, machine learning models do. This happens as models begin to generate predictions from data that changes and adapts. MLOps therefore includes procedures that facilitate continuous monitoring and retraining so that algorithms can continue to be used in production environments. 

Why is MLOps important?

Producing reliable machine learning models is extremely difficult because the machine learning lifecycle consists of many complex components. Data ingestion, data preparation, model training and tuning, model deployment, and model monitoring; there is an incredible amount of work that goes into producing even the simplest model. 

Producing machine learning models also requires strong collaboration across teams including data science, data engineering, ML engineering, and IT. Due to the inherent complexities involved, machine learning model development requires stringent operational management to keep all these processes in sync and working together.

MLOps is the process that encompasses all of this (and then some) to drive continuous improvement of the machine learning lifecycle and help ML teams deploy the very best models that they can.

MLOps builds on DevOps principles

Though powerful, MLOps isn’t some sort of new and revolutionary idea. It borrows many principles from its close relative, DevOps. In essence, MLOps is a specific implementation of DevOps in that it is DevOps for machine learning projects and pipelines. If you are somebody who is familiar with DevOps, you will find it much easier to pick up the concepts of MLOps if you keep in mind the differences that we covered above. 

Qwak is an end-to-end machine learning platform that helps you manage data preparation, machine learning, operations steps, and more, all from a single interface. Using Qwak, businesses can develop better ML models faster. 

Want to find out more about how Qwak could help you deploy your ML models effectively and efficiently? Get in touch for your free demo!