MLOps vs. DevOps: A Comprehensive Guide
The word machine learning is buzzing so loud that almost every IT professional has heard this term by now. With time, machine learning has become more applied, and every industry is leveraging it. Many significant projects, applications, and AI-driven software run ML algorithms, and hence engineers are confidently pushing it to production. It brings us to the concept of MLOps. It might sound similar to DevOps, but both are technically different from each other. In this article, you will know how DevOps is different from MLOps.
Understanding DevOps –
DevOps is a composite of development (Dev) and operations (Ops). It is a technique that brings together the various systems' development life cycles. It aims to shorten the development process involving designing, production, testing, and operation. DevOps aims at turning the traditional siloed processes into agile and continuous phases through automation, continuous delivery (CD), and feedback loops.
Understanding MLOps –
MLOps, on the other hand, is the development life cycle involving automating and product development through machine learning. Often the machine learning product development goes entirely separate from the operations. MLOps is the technique that streamlines the workflow of ML-based product development from beginning to end. The main focus lies in bridging the gap between design, ML-model development, and operations.
Difference between MLOps and DevOps
Although MLOps applies the principles of DevOps, there are significant differences between each of them. DevOps comes from software engineering concerning the development and operations of large-scale software production. MLOps, on the other hand, unites data collection, data preprocessing, data modelling, evaluation, product deployment, and re-training into a unified process.
Again, MLOps and DevOps development pipelines include the code-validate-deploy loop; MLOps also contains additional phases of data preprocessing, modelling, and training algorithms. Thus, you can say that MLOps carries a few other nuances for each workflow component that are missing in traditional DevOps. The additional phases of MLOps that separates it from DevOps are:
· Extraction of Data
· Data analysis
· Preparing data for modelling
· Training the model/algorithm
· Evaluating the model
· Serving the model
· Monitoring the model
MLOps helps better communication & collaboration of system administrators, data science team, and other development teams. It brings all the different departments under one page and allows us to understand how to model the production and maintain it. It is somewhat related to how DevOps does for software development. Here are some of the key points that bring in some other significant differences between MLOps and DevOps.
i. MLOps is much more data-driven and experimental. Data scientists and machine learning developers keep on trying a model with different parameters, features, and statistical algorithms. All its iterations must create and manage reproducible results. DevOps are also experimental but do not get fully implemented in the primary production.
ii. The involvement of data brings another significant difference between MLOps and DevOps. Traditional software does not take data as input. Even DevOps also focuses on the coherent running of development and testing along with other operations. But modern applications are ML-based. Thus, they incorporate data in addition to coding. Applications that run ML models will utilize a large quantity of data.
iii. Monitoring is another crucial step in the development of an ML-driven algorithm. While DevOps focuses on delivering applications and services at high velocity, it tightly integrates the development, testing, and operation, making the complete lifecycle limited to certain constraints. In the case of MLOps, ML projects constantly undergo data transformation. As a result of constant changes in data, the ML model also needs frequent adjustments and updates. It increases the production time, especially if the data has seasonality.
iv. Unlike DevOps, MLOps comes with a machine learning model that produces accurate predictions with time. In DevOps, the developer and the operation team drive the Continuous Integration and Continuous Deployment (CI/CI), which is time-consuming. But in MLOps, the development team runs the automation that leverages the training and re-training process. It ultimately improves the time to market for the projects that render ML models.
v. Continuous testing in DevOps is comparatively fast because it simply validates the workflow and quality, along with the business risks associated with the software. Continuous testing is automated testing that employs unit testing and integration testing. On the other hand, testing an ML-based system involves model validation, model training tests, etc., in addition to application testing done in DevOps. That slightly increases the testing time in the case of MLOps.
vi. Versioning is another significant differentiation that developers can witness while developing ordinary software and ML-based software. In a production that follows DevOps, version control ensures clear documentation about the changes or adjustments made in the QA phase. With ML-based production, the code is not the only entity that changes in the QA phase. Data also plays a critical role that comes into consideration along with the parameters, metadata, and model.
Roles of Team members in DevOps and MLOps –
There is a slight difference between the roles and responsibilities between MLOps and traditional DevOps. In the principle of DevOps, the software engineers are responsible for developing the codes while DevOps engineers focus on deploying and constructing a CI/CD pipeline. But this is not the case in MLOps. In MLOps, machine learning engineers and data scientists play a significant role in the development phase. They write the code to build the ML models & train them with different data sets. They are also responsible for the deployment and proper monitoring of these models during production.
Conclusion
We hope this comprehension guides you in showing the different aspects of DevOps and MLOps. It is important to note that no successful software development firm can operate without adopting any DevOps principles. Furthermore, if an application deals with dynamic data and machine learning models for better prediction and accuracy at work, MLOps principles play a significant role. Machine learning engineers go through the MLOps lifecycle to validate the data and the schema, along with the performance of models in production. Hence, both have their significance in the development of the application. Learn how to leverage pre-made projects of DevOps and MLOps from ProjectPro.
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