.png){kind=logo}
DevOps speeds up software delivery while ensuring stability and reliability in applications.
MLOps manages models and data to maintain accuracy, fairness, and adaptability in AI systems.
DevOps and MLOps build reliable software with smart and evolving machine learning models.
Technology is changing quickly, and software along with artificial intelligence is now part of everyday life. Companies need systems that stay reliable and can adjust to updates. DevOps is used for creating and improving software.
MLOps is used for handling machine learning models. Both serve different needs, and many businesses use them together for better outcomes. Let’s take a look at what each field brings to the table.
DevOps stands for development and operations. In earlier days, developers wrote code while operations teams managed servers, and both groups worked separately. This caused delays, errors, and confusion. DevOps brings them together and uses automation to speed up the whole process.
With DevOps, the cycle of building, testing, and releasing software becomes smoother. For example, a mobile banking app needs frequent security updates. DevOps pipelines help by automatically checking the new code, testing it for errors, and releasing it to users. This reduces downtime and keeps the app safe and reliable. The main goal of DevOps is faster delivery and better stability.
Also Read: DataOps vs. DevOps: Key Differences and Use Cases
MLOps is based on the same principles as Developer operations, but is designed for machine learning. Machine learning depends on both code and data. A model trained on one dataset may lose accuracy when new data appears. This makes the process more complicated than normal software development.
Take an online shopping site as a use case. A model that recommends products works well at first, but becomes less accurate if customer habits change. MLOps creates a proper system for training, testing, deploying, and retraining these models. It also records datasets, experiments, and results so teams can track every step clearly.
There are several key differences between DevOps and MLOps:
Focus: DevOps handles only code, while Machine Learning operations manage both code and large datasets.
Lifecycle: Developer operations follow a cycle of build, test, deploy, and monitor. MLOps adds steps such as data preparation, training, and retraining models.
Monitoring: DevOps checks system errors, uptime, and server health. MLOps also checks accuracy, fairness, and signs of model drift.
Infrastructure: Developer operations uses tools like Docker, Kubernetes, and CI/CD pipelines. MLOps needs all of these, along with GPUs, feature stores, and frameworks like MLflow or Kubeflow.
Team Structure: DevOps teams usually consist of developers and operations engineers. MLOps teams add data scientists, ML engineers, and subject experts.
Also Read: Mastering MLOps in 2025: A Step-by-Step Roadmap
DevOps works best for regular software projects such as social media apps, messaging services, or online payment systems. These rely on frequent code updates and stable performance.
MLOps is better for projects that depend on models and data, like fraud detection in banking, speech recognition systems, or autonomous vehicles. These require models to adapt to new data while staying accurate and fair.
Many companies combine both fields for greater results. A ride-hailing app is a good example. DevOps manages the app while MLOps runs the pricing models and driver-matching systems. By working together, both methods cover every part of the application.
DevOps changed the way software is delivered by making it faster and more reliable. As machine learning became common, MLOps was created to handle the challenges of data and model management.
The former makes sure that strong and stable applications are built, while the latter keeps models useful and accurate over time. When used together, these compounded concepts provide the best results, especially in today’s world where software and artificial intelligence often go hand in hand.
