MLOps vs DevOps: Understanding the Differences & Similarities?
You’ve probably heard of DevOps, especially if you work in the tech industry but you might not be familiar with MLOps. MLOps is a more recent discovery in the field of machine learning, but it is gaining traction thanks to its successful application of traditional DevOps principles.
Although these two fields are related, as suggested by their similar names, they also have some significant distinctions that you should be aware of.
This article will be covering the following contents:
- What is DevOps?
- What is MLOps?
- MLOps vs. DevOps: Differences
- MLOps vs. DevOps: Similarities
What is DevOps?
Software development and IT operations are referred to collectively as DevOps, and the phrase refers to both the activities and tools that make up DevOps as well as the cultural mentality that underpins them. DevOps marked a significant movement in the IT industry, moving away from labor-intensive, slow procedures and toward quicker, iterative development.
Many well-known tech concepts, such as continuous delivery, infrastructure as code, and Agile development approaches, originated or developed from DevOps Framework. Another driving force behind breaking down team-based divisions and bringing software and IT professionals closer to the rest of the business and the customers was DevOps.
Software development, integration, testing, deployment, and monitoring are all parts of the DevOps lifecycle, which is carried out repeatedly until the required level of quality is reached for the software product. The DevOps lifecycle’s goal is to build, iterate, and release the best software as soon as feasible.
What is MLOps?
MLOps stands for machine learning operations that take the same principles of DevOps and apply them to the field of machine learning. In this case, the machine learning model is substituted for the software product.
The machine learning model also goes through the same development, integration, testing, deployment, and monitoring processes as software in DevOps, despite this distinction. The autonomous deployment of machine learning models into software systems is the ultimate objective of MLOps.
Compared to DevOps, MLOps is a relatively recent development, and the industry still needs time to develop. The fact that machine learning and artificial intelligence have just recently started to evolve quickly, driven by better hardware improvements and accessibility, accounts for a portion of this.
Over the next few years, the field of MLOps will continue to develop as machine learning technology develops and as teams discover new methods to incorporate DevOps practices into machine learning.
MLOps vs DevOps: Differences
The primary differences between MLOps and DevOps come down to specific development processes, version control, monitoring, and team members.
· Development processes
A software application or interface is developed, deployed, and put through a number of tests as part of the DevOps process. Up until the finished product reaches the specified objective, this process is repeated.
In MLOps, creating and training a machine learning model counts as “coding,” and comparing the trained model to the test model counts as testing. This cycle keeps going until the model operates with the anticipated level of accuracy.
In MLOps, the method of development is significantly more exploratory. Although the procedure might appear to be written down, due to the more fluid nature of the machine learning model, things frequently change in practice.
· Monitoring
After the initial development phase, monitoring is an important duty that aids in maintaining the quality of a product. Monitoring in DevOps involves not only the software product itself but the entire lifecycle, from planning to deployment. The monitoring of the machine learning algorithm itself, on the other hand, is the main focus of MLOps, which looks primarily for data drift and accuracy flaws that depart from the expected outcomes.
· Version Control
Version control is crucial for recognizing and resolving faults that result from those modifications since it entails tracking and controlling changes to the software product.
Version control in DevOps is usually simpler because it includes keeping track of changes made to the code and artifacts. Tracking modifications to the model code, training input data, experiment run, and other components are part of version control for MLOps.
Version control, however, is as crucial in MLOps due to the dynamic nature of machine learning models. The performance of the model may eventually deteriorate with any new addition. Contrast this with DevOps, where if a piece of code is merged incorrectly, the software itself doesn’t degrade, making it simpler to locate the mistake and fix it.
· Team members
Both DevOps and MLOps teams have distinct roles and responsibilities because of the differences in the final output. Software developers who create the product and DevOps engineers who deploy it typically make up DevOps teams. In contrast, MLOps teams often include machine learning engineers who deploy the model and data scientists who train the model.
MLOps vs DevOps: Similarities
DevOps and MLOps do have certain similarities, which makes sense given that MLOps is founded on DevOps principles.
MLOps uses the same lifecycle stages and concepts as DevOps, but it applies them in a different environment using machine learning models. Many of the same tasks are covered by both DevOps and MLOps procedures, including version control and monitoring, and their team structures might resemble one another.
However, because software applications and machine learning models are essentially different products with distinct difficulties, many of these procedures appear different in practice. The subject of machine learning is continually changing as the most efficient approach to go both generally and when it comes to using DevOps concepts specifically.
In the next article, I'll be talking about the different MLOps tools.
References:
