What is MLOps vs DevOps in Modern Software Engineering?

DevOps focuses on improving how software is built, tested, and deployed. It connects development and IT operations so teams can release applications faster while keeping systems stable. 

MLOps builds on these ideas for machine learning systems. It manages the full model lifecycle, including data handling, model training, validation, deployment, monitoring, and retraining as new data appears. 

In this blog you will understand what is MLOps vs DevOps in detail, how they work, where they differ, and when teams use each approach. 

If you want to go beyond the basics of MLOps and build real expertise, explore upGrad’s Artificial Intelligence courses and gain hands-on skills from experts today!         

Understanding the Core Difference: What is MLOps vs DevOps? 

The easiest way to understand what is MLOps vs DevOps is to compare how each approach manages development, deployment, and monitoring. 

Below is a clear comparison between both: 

Comparison Table 

Aspect	DevOps	MLOps
Focus	Building and deploying software applications	Managing machine learning systems
Primary goal	Faster software releases with stable systems	Reliable deployment and maintenance of ML models
Core artifact	Application code	Code, datasets, trained models
Pipeline structure	Continuous Integration and Continuous Deployment pipelines	Data pipelines, model training pipelines, and deployment workflows
Testing approach	Code testing and integration testing	Data validation, model testing, and performance evaluation
Monitoring	System uptime, logs, and application performance	Model accuracy, prediction quality, and data drift
Updates	Deploy new code versions	Retrain models when data patterns change
Team roles	Developers, operations engineers, site reliability engineers	Data scientists, ML engineers, data engineers

Key idea:  

• DevOps manages software delivery  
• MLOps manages machine learning lifecycles  

Many AI products use both approaches together.  

Also Read: SDLC Guide: The 7 Key Software Development Life Cycle Phases Explained 

What is MLOps 

To understand What is MLOps vs DevOps, you first need to know what MLOps means and why it exists in machine learning systems.  

MLOps stands for Machine Learning Operations. It is a set of practices used to manage the complete lifecycle of machine learning models. The goal is to help teams build, deploy, monitor, and update models in a structured and repeatable way. 

Without MLOps, teams often struggle with issues like inconsistent data, models that stop performing well, or deployments that break existing systems. 

Also Read: Automated Machine Learning Workflow: Best Practices and Optimization Tips 

Key Components of MLOps 

MLOps manages several stages in the machine learning workflow. 

• Data management 
  Collecting, cleaning, and versioning datasets used for training models. 
• Model training 
  Training machine learning models using prepared datasets. 
• Model validation 
  Testing models to check accuracy and performance before deployment. 
• Model deployment 
  Deploying trained models so applications can use them for predictions. 
• Monitoring and retraining 
  Tracking model performance and retraining when accuracy drops. 

These steps help teams maintain stable machine learning systems. 

Also Read: Top Machine Learning Skills to Stand Out 

Typical MLOps Pipeline 

A basic MLOps workflow usually follows these stages: 

Stage	Description
Data collection	Gather data from databases, APIs, or logs
Data preprocessing	Clean and prepare data for training
Model training	Train machine learning models
Model evaluation	Check model accuracy and performance
Model deployment	Deploy model for real-world predictions
Model monitoring	Track performance and detect drift

Common MLOps Tools 

Teams often use specialized tools to manage machine learning pipelines. 

• MLflow for experiment tracking and model management 
• Kubeflow for managing ML workflows on Kubernetes 
• TensorFlow Extended (TFX) for production ML pipelines 
• Apache Airflow for workflow orchestration 
• DVC for dataset version control 

These tools help automate machine learning workflows and maintain model reliability in production systems. 

What is DevOps 

To understand what is MLOps vs DevOps, you also need a clear idea of DevOps and how it supports modern software development. 

DevOps (Development and Operations) is a set of practices that connects software development teams with IT operations teams. The goal is to build, test, and deploy applications faster while maintaining stable and reliable systems. 

In the discussion of what is MLOps vs DevOps, DevOps focuses on managing application code and infrastructure rather than machine learning models and datasets. 

Also Read: DevOps Career Path: A Comprehensive Guide to Roles, Growth, and Success 

Key Components of DevOps 

DevOps covers several stages of the software development lifecycle. 

• Version control 
  Managing source code using systems like Git so teams can track and review changes. 
• Continuous Integration (CI) 
  Automatically testing and merging code updates into a shared repository. 
• Continuous Deployment (CD) 
  Automatically releasing tested code to production environments. 
• Infrastructure management 
  Automating server setup, cloud resources, and container environments. 
• Monitoring and logging 
  Tracking system performance and identifying issues after deployment. 

These steps help teams deliver software updates quickly and safely. 

Typical DevOps Pipeline 

A common DevOps workflow follows a structured pipeline. 

Stage	Description
Code development	Developers write and update application code
Version control	Code stored and tracked using Git repositories
Continuous integration	Automated builds and tests run after code updates
Deployment	Applications are deployed to servers or cloud environments
Monitoring	System performance and logs are tracked

This automated pipeline helps teams release updates frequently without breaking the system. 

Also Read: Devops Engineer Salary in India for Freshers, Experienced 

Why DevOps Matters 

Modern applications require frequent updates and reliable systems. DevOps helps teams handle these requirements by improving collaboration and automation. 

Benefits include: 

• faster software releases 
• fewer deployment errors 
• consistent environments across development and production 
• better monitoring and system reliability 

When comparing what is MLOps vs DevOps, DevOps focuses on application delivery while MLOps expands these practices to manage machine learning workflows. 

Also Read: Future Scope of DevOps – 15 Reasons To Learn DevOps 

Common DevOps Tools 

DevOps teams rely on several widely used tools. 

• Git for version control 
• Docker for containerization 
• Kubernetes for container orchestration 
• Jenkins for CI/CD pipelines 
• Terraform for infrastructure automation 

These tools help automate development pipelines and manage large-scale applications. 

When Should Teams Use MLOps vs DevOps 

Many teams ask this after understanding what is MLOps vs DevOps. The answer depends on the type of system you are building. 

Use DevOps when 

Your project focuses on traditional software systems. 

Common scenarios include: 

• Building web applications 
  DevOps pipelines automate testing and deployment for web platforms. 
• Deploying APIs or backend services 
  Continuous integration and deployment pipelines manage frequent code updates. 
• Managing microservices architectures 
  DevOps tools help run containerized services and maintain system stability. 

Also Read: 40 DevOps Examples: Exploring Key DevOps Use Cases 

Use MLOps when 

Your system relies on machine learning models and data pipelines. 

Typical cases include: 

• Deploying machine learning models 
  MLOps pipelines manage training, validation, and deployment of models. 
• Systems that learn from data 
  Recommendation engines, fraud detection systems, and predictive analytics platforms require data-driven models. 
• Models that need retraining over time 
  Data patterns change, so models must be monitored and retrained regularly. 

Using Both Together 

Many modern AI products combine both practices. 

Example architecture: 

• DevOps manages infrastructure such as cloud environments, containers, and APIs. 
• MLOps manages model pipelines, including data preparation, training workflows, and model monitoring. 

This combined approach helps organizations maintain stable applications while running machine learning systems in production. 

Also Read: Is DevOps Easy to Learn? : Challenges and Tips to Become a DevOps Expert 

Conclusion  

Understanding what is MLOps vs DevOps helps teams build reliable AI systems. DevOps improves software delivery pipelines. MLOps manages the lifecycle of machine learning models and data pipelines. When combined, they allow organizations to deploy intelligent applications while keeping systems stable and scalable.  

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