What is The Difference Between MLOps And DevOps?

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In software and machine learning development, two methodologies often come into focus: DevOps and MLOps. While these terms sound quite similar, they are related to different worlds. These approaches may be of similar note, although each encompasses distinct phases of the development cycle. Understanding the two concepts becomes crucial for organizations that want to enhance their performance and direct their technical might to the best extent.

Every organization’s goal is to cut across all sorts of businesses in today’s technological environment and address the question: How do I shorten my cycle time while simultaneously delivering value to my stakeholders? Therefore, the practices of DevOps and MLOps have been recognized as key processes to help organizations achieve these objectives. While DevOps has been a practice name for over a decade now, MLOps, on the other hand, is relatively new and has emerged within a few years to cater to all the requirements unique to Machine Learning practices.

Defining MLOps And DevOps


MLOps, or Machine Learning Operations, can be described as the extension of DevOps or the pursuit of Machine Learning practices. Some of the activities and solutions it encompasses are relevant to ensuring a machine learning model can smoothly be deployed in a production-like environment. Hence, we can say that MLOps centers widely on ensuring that developing, training, and deploying machine learning models are as easy as building other applications.


‘Development’ + ‘Operations’ In this context, DevOps refers to a concept that seeks to facilitate proper coordination and structural relations between development and operations staff. It classifies deployment automation into teamwork, C/I, C/D, and general.

 Let it be stated that DevOps's core value proposition is to minimize the time taken to deliver software and let the customer derive value through providing quality software. 

Core Functionalities: MLOps Vs. DevOps

Let us look at a comparative table of the two:




Collaboration & Integration

MLOps is an intersection of data science, machine learning engineering, and operations. It coupled data pipelines, training, and serving with tools such as TensorFlow Extended (TFX), MLflow, Kubeflow, and Databricks. 


DevOps refers to practices used with development and operations to manage code updates, testing, and deployment effectively. The flow is automated using technologies like Jenkins, Git, Docker, and Kubernetes.

Continuous Integration And Deployment (CI/CD)

The MLOps CI/CD pipeline applies to code integration but extends to model training and validation. The other feature is automated retraining and deployment; this is a feature that demands models to be reviewed to reflect the changes that are prevailing.

In the DevOps process, CI stands for continuous integration, and CD stands for constant deployment, which pertains to code integration, testing, and deployment. This is useful for improving the ability to incorporate new code

changes into the production environment.

Monitoring And Maintenance

Monitoring in MLOps is also for machine learning models, allowing us to keep track of accuracy, model drift, and performance. It employs tools like Seldon, Fiddler, and Arize AI to check whether models function as intended in the production environment.

DevOps is also a continuous process of looking for issues and enhancing the application and infrastructure for stability. Some of these tools include Prometheus, Grafana, and the ELK stack. 


Data Management

Another major aspect of MLOps is related to data handling, including ingestion, preprocessing, feature engineering, and versioning. Correct data management is very crucial to guarantee the possibility of reproducing machine learning experiments. 

In DevOps, there is a specific focus on managing code and infrastructure, also called infrastructure as code. Data management, in general, is usually done by other data

engineering groups.

Scalability And Flexibility

Modern MLOps frameworks assist with large-scale model training and deployment, which can leverage different computing and hardware, such as distributed systems, GPUs, and TPUs.Based on DevOps, one can deploy applications on a wide scale by employing Docker and Kubernetes, among others. 

Tools And Technologies

In MLOps, TensorFlow Extended (TFX) and MLflow help manage the ML/application-development life cycle. Kubeflow is developed using Kubernetes to address machine learning requirements specifically and offer the flexibility to scale up and manage the models. Moreover, data versioning tools such as DVC (Data Version Control) ensure that data needed for training models is adequately handled.

DevOps, similar to MLOps, uses different tools to manage and automate various aspects of the pipeline. DevOps Key tools are Jenkins, used for integration; Docker,

used in containerization; and Kubernetes, used in orchestration. These tools assist in managing the deployment pipeline and delivering software with velocity and quality.


Main Distinctions Of MLOps And DevOps

MLOps and DevOps are both used in software delivery, but they are used for different purposes in the Software Development Life Cycle (SDLC). The following is a list of significant differences between the two: model training, data use, model evaluation, and feedback and their differences:

Model Training And Experimentation

Model training and experimentation are MLOps’ most important strengths. MLOps practices help with organizing the training and prototyping of machine learning models. It is about choosing correct models, adjusting various parameters, and taking care of the model training process. MLOps engineers experiment with different training settings and use tools like Tensorflow, Pytorch, and Kubeflow to automating the training process.


DevOps is not directly involved in model training or testing. It is centered around the simplicity of the development and deployment of conventional software applications.

Data Dependencies

Information is the essence of machine-learning algorithms. In MLOps, there is more focus on the quality of data at each stage of the ML process. This relates to activities such as data importation, data scrubbing, data update, data management, and data synchronization, respectively. It also helps solve problems data bias problems and guarantees that the models are trained on reasonably balanced data.


Data may be used within applications created using DevOps, although data quality is not their focus. DevOps is mainly concerned with the correctness of code and whether the application runs as expected when fed data.

Model Monitoring And Feedback

Monitoring of the models that go to production is a key theme in MLOps. This entails identifying and solving problems such as model drift, where the performance of the model degrades over time as a result of changes in data distribution to ensure that model accuracy falls within an expected range at any given time. MLOps tools and techniques provide signals as to when models should be retrained or updated on when they are not effective anymore.


Overseeing application performance and infrastructure status is one of the significant DevOps considerations. However, this monitoring concentrates on conventional software KPIs such as availability, resource consumption, and errors. In general, DevOps does not imply the need to track the performance of machine learning models.  

MLOps And DevOps Trends In The Future

Since technology is rapidly expanding, DevOps and MLOps are also likely to experience more developments in the future. Then, there is the serverless computing model and the use of

artificial intelligence in the automation of tasks, which will likely define the future of DevOps. These trends will build upon the efficiency and scalability of DevOps practices even further.

In MLOps, Artificial intelligence and machine learning will progress in model tracking, interpretability, and fairness. Also, the inclusion of edge computing will allow the learned

models to be deployed closer to the data source, hence reducing latency.

Key Takeaways

Even though DevOps and MLOps are similar in terms of automation, collaboration, and integration, they serve different stages of the development process. DevOps is concerned with improving software development and release flow, whereas MLOps applies these ideas to machine learning pipelines' more dynamic and iterative character. It is crucial for organizations interested in adopting these methodologies to evaluate their requirements and choose the tools and practices that will suit them best. To harness the full potential of software and machine learning projects, it is critical to understand the differences and synergy between DevOps and MLOps.

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