MLOps vs. DevOps: Key Differences — and Why They Work Better Together
Explore the differences between MLOps and DevOps — and why using both can supercharge your software and AI workflows.
In the age of AI, there's a new "Ops" in town: MLOps, the latest, greatest twist on DevOps.
This raises the following questions: What is MLOps? How does it relate to DevOps? And how can you leverage both flavors of "Ops" at the same time? Read on for perspective as we compare MLOps and DevOps.
What Is MLOps?
MLOps, short for machine learning operations, is a set of practices and strategies for streamlining the deployment and management of AI models .
The exact practices at the heart of MLOps vary a bit depending on whom you ask, but they typically cover three main areas:
Model design, or determining what an AI model should do, which type of model architecture to use, and which data the model will need.
Model development, the process of actually implementing the model. This includes writing the model's code, training the model, and performing post-training evaluation and tweaking.
Model operations, or the workflows necessary to place the model into a production environment where it can perform inference and maintain it once it's there.
DevOps' Influence on MLOps
If you're familiar with DevOps — a philosophy that encourages tight collaboration between software developers and IT operations teams — you know that DevOps also goes hand in hand with a distinct set of practices that are organized in a certain way. These include the various stages of software development defined in the software development life cycle (SLDC), as well as practices like continuous integration/continuous delivery (CI/CD).
Related:AI Code Generation Creates Blind Spots in DevSecOps Security
The similarities between MLOps and DevOps in this respect are no accident. MLOps — a term that was coined in 2015 , when DevOps was arguably the buzziest topic in tech — deliberately builds on DevOps concepts. Much as DevOps brought order and consistency to the fraught and risk-prone processes at the core of software development, MLOps helps streamline complex AI model design, development, and deployment operations.
What Makes MLOps Different From DevOps
That said, MLOps is distinct in many other ways from DevOps. Key differences include the following:
1. Philosophical goals
Arguably, the greatest difference between DevOps and MLOps is that DevOps is, by most definitions, an abstract philosophy, whereas MLOps comes closer to prescribing a distinct set of practices.
Ultimately, the point of DevOps is to encourage software developers to collaborate more closely with IT operations teams, based on the idea that software delivery processes are smoother when both groups work toward shared goals.
Related:DevSecOps 2.0: How Security-First DevOps Is Redefining Software Delivery
In contrast, collaboration is not a major focus for MLOps. You could argue that MLOps implies that some types of collaboration between different stakeholders — such as data scientists, AI model developers, and model testers — need to be part of MLOps workflows. Still, MLOps doesn't provide philosophical guidance related to collaboration and teamwork as much as it offers a systematic approach to creating and operating models.
2. Area of focus
Another key difference is that DevOps centers solely on software development. MLOps is also partly about software development to the extent that model development entails writing software. However, MLOps also addresses other processes — like model design and post-deployment management — that don't overlap closely with DevOps as traditionally defined.
3. Skill sets
Differing areas of focus lead to different skill requirements for DevOps versus MLOps. To thrive at DevOps, you must master DevOps tools and concepts like CI/CD and infrastructure-as-code (IaC).
Those skills may also be helpful in some MLOps contexts, but the primary areas of expertise associated with MLOps center on data engineering, model development, and model testing. A skilled DevOps practitioner is not necessarily an MLOps expert, and vice versa.
Related:Guide to DevOps Topologies
Combining DevOps and MLOps
The fact that DevOps is distinct from MLOps doesn't mean these are mutually exclusive philosophies or practices. On the contrary, most organizations are likely to find that they can maximize the value of DevOps and MLOps when they use both simultaneously.
In most cases, such an approach would entail using DevOps principles to streamline software development — including the development of AI models. At the same time, MLOps practices would guide the areas of AI design and management that DevOps doesn't address.
The result is an efficient and scalable approach to software development workflows of all types — including but not limited to those that center on AI models and machine learning.
About the Author
Technology analyst, Fixate.IO
Christopher Tozzi is a technology analyst with subject matter expertise in cloud computing, application development, open source software, virtualization, containers and more. He also lectures at a major university in the Albany, New York, area. His book, "For Fun and Profit: A History of the Free and Open Source Software Revolution ," was published by MIT Press.
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