By 2025, nearly 78% of organizations worldwide have added DevOps, making it the go-to approach for delivering software faster with fewer glitches.
Automation is no longer just an option; most tech-driven companies are asking how to scale it. At the same time, MLOps has stepped into the spotlight as a field of machine learning. They have moved beyond experiments into real-world products. Businesses have learned that building software and running AI systems are different challenges. One of them focuses on stable code releases, while the other manages models that change with data updates.
With an understanding of DevOps ending and MLOps beginning, teams can avoid costly mistakes, slow deployments, and unreliable AI outcomes.
Understanding DevOps and MLOps
DevOps is not a tool or a single process. It is a mindset where development and operations teams stop working as separate units and start owning the software together. Instead of developers handling the code and hoping it works in production, DevOps promotes collaboration from the first line of code to live deployment. Practices like continuous integration, automated testing, and continuous delivery help catch problems early and release updates faster.
MLOps is built on DevOps ideas, but the application is different. Machine learning systems never rely on being ‘Done’ after installation. Instead, they continue to change models, slowly losing accuracy without anyone noticing. It covers everything from collecting and validating data to training models, tracking experiments, deploying them safely, and monitoring their behavior in the real world.
A Comparison Table Explaining The Difference
|
Feature / Aspect |
DevOps |
MLOps |
|
Core Focus |
Software development & delivery |
ML model lifecycle from data to deployment |
|
Primary Artifacts |
Code, binaries, configs |
Datasets, feature sets, models, experiments |
|
Pipeline Stages |
Build, test, deploy, and monitor |
Data prep, training, validation, deployment, and monitoring/retraining. |
|
Monitoring Metrics |
Uptime, latency, errors |
Predictions accuracy, drift, bias |
|
Team Composition |
Developers with operations |
Data scientists with ML engineers and operations. |
|
Infrastructure Focus |
IaC, containers, virtual machines |
Add GPUs, data storage, and distributed training |
|
Versioning |
Code & config only |
Code with model, data, and experiments. |
|
Model Retaining Triggers |
Not required |
Requires rules or alerts triggering retraining. |
|
Data Governance |
This typically limited reliable training outcomes. |
Requires strict data quality checks and lineage tracking. |
What Is The Importance Of These Differences
-
Data vs Coda-Centric Workflows
Traditional DevOps primarily manages static software artifacts, leading to predictable outputs after deployment. In contrast, MLOps revolves around data-driven artifacts that change over time, making data versioning and governance essential.
This includes strict data quality checks, lineage tracking, and retraining rules, ensuring models remain reliable and compliant throughout their lifecycle.
For clarity, imagine an e-commerce recommendation system with DevOps alone. You deploy website features. With MLOps, you need to constantly update the recommendation model, as changes in user preferences require automated retraining and performance monitoring to ensure high-quality recommendations.
-
The Differences In Tooling And Pipeline Complexity
DevOps automation focuses on CI/CD for coding and environment setup. It uses tools such as Jenkins, GitLab CI, and Terraform to streamline the build and release cycles. MLOps requires additional support through data pipelines, experiment tracking, model validation, and drift monitoring. It uses tools like MLflow, Kubeflow, or Airflow.
-
Monitoring And Continuous Learning
Monitoring in DevOps focuses on system performance, uptime errors, and response time. Whereas MLOps monitors metrics such as prediction accuracy, data drift, and bias post-deployment. This is because ML models can degrade if the underlying data distribution changes over time.
88% of organizations report using AI regularly in at least one business function. This shows us the widespread adoption of AI in the enterprise.
For instance, a financial fraud detection system performs well initially, but over time, it begins to miss new fraud patterns as the data shifts. The competent MLOps pipelines detect this drift and automatically trigger retention.
-
Team Structure and Organizational Impact
DevOps promotes collaboration between developers and operations teams, aligning them closely with IT and product teams. Roles include DevOps engineers, SREs (Site Reliability Engineers), and platform engineers. Their role is to shorten delivery cycles and enhance system reliability.
Whereas MLOps teams combine data scientists, data engineers, ML engineers, and operations specialists, highlighting the value of diverse skills, making readers feel their expertise is crucial for success.
This is one of the reasons that MLOps projects require domain expertise with traditional software engineering skills. DevOps focuses on coding and system operations.
Final Words: Bringing It All Together
Towards the end of 2025, the difference between DevOps and MLOps has become clearer. DevOps is the backbone of software automation and delivery. MLOps is expanding its principles to manage machine learning lifecycle complexities. These include involving data, models, and retaining.
DevOps is based on predictable code deployment and system monitoring, while MLOps adapts to the dynamic data and changing model behaviour. Whether it is tooling, team structures, or performance tracking, the two approaches focus on different artifacts and priorities, even while sharing goals such as automation and rapid organization.
And if you want to stay ahead in this revolving landscape, you should consider Gravity Base. It merges traditional DevOps and MLOps workflows and reduces optional complexity, driving stronger collaboration.
FAQs
When do I need MLOps even with DevOps?
DevOps works when you are installing code. You will need MLOps when your product includes machine learning models that adapt to changes in data.
Can MLOps work with existing DevOps workflows?
MLOps will not replace DevOps. It is built into it. DevOps handles software delivery, while MLOps manages data, models, and retraining cycles.
Why do machine learning models require monitoring and retraining after installation?
As real-world data changes with time, models can lose accuracy without warning. Monitoring helps detect the issue early, and retraining keeps the prediction reliable.
What are the tools and skills required when moving from DevOps to MLOps?
MLOps require tools for data pipelines, experiment tracking, and model monitoring. The team even needs skills in data engineering, ML, and model evaluation.
How can businesses reduce the complexity while managing both DevOps and MLOps?
With a unified platform, teams can manage code, data, and models in a single workflow. This reduces silos, manual work, and operational burdens.
