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DevOps Vs MLOps Comparative <br> Analysis <br> You Should Know

DevOps Vs MLOps Comparative
Analysis
You Should Know

02nd Dec, 2021

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This blog is an attempt to help you understand DevOps and MLOps and their differences and similarities.

 

 

What is DevOps?

DevOps is defined as the integration of development, testing, and operations of software development processes. The major purpose of DevOps is to make an integrated or continuous process in the organization for smooth working.

 

Goal of DevOps?

The primary goal is to do automation, a continuous cycle of the process, and then feedback. This effort is dependent on communication between the different departments of the business and tools used for integration in between the process.

 

CI and CD in DevOps?

Continues Integration CI is the automation of code transformation from multiple people into single integration of a project.

 

Continuous Delivery (CD)

Continuous delivery shortens delivery cycles, increases deployment speed and make dependable releases

 

What is MLOps?

MLOps also called machine learning operations, are the practices and processes which are used to streamline the machine learning cycle from start to end.

 

The goal of MLOps?

The primary goal or purpose of MLOps is to bridge the gap between design, model training, deployment, and operations. The main process focus is to do more effort on the training and development so that no problem will arise during the operation of the model.

 

MLOps and ML model management involves development and operations, but they are both separate and combined later on.

 

MLOps gives some type of standard for data collection, preprocessing, training of the model, model deployment, and then the real-time operation of the model. MLOps’s purpose is to manage the deployment of ML models at a larger scale with some uniform practices and processes.

 

There is a difference between MLOps and DevOps in their work. Some of the comparative analyses are presented below.

 

 

Experimentation

MLOps is exploratory and experimental based in nature as compared to DevOps. The machine learning models are experimented with, trained, and then tested for performance. The model is selected based on the best performance and analysis metrics.

 

Data Usage

Machine Learning models are created by running or simulating the algorithm on large information converted into coding directions. The synchronization of data and code is difficult due to some limitations in coding and some limitations in data. Feature engineering and data engineering help us to sort out that problem.

 

ML pipelines

Data pipelines are defined as the sequence of transformations which occur in data from when it starts to transform till the end. Data pipeline provides a lot of benefits like scalability, run-time visibility, reuse of code, and administration. Machine Learning pipelines are easy to code do not depend on data and can be handled using the basic DevOps technique of the CI/CD pipeline

 

Monitoring

Using the DevOps approach the collection of monitoring data is important before starting production. To check the standard metrics are necessary like latency, traffic, errors to control architecture. However, Machine learning monitoring is difficult and relies on the trained data which cannot be changed. So in the ML case, the prediction performance is checked only with some features

 

Data validation

The data pipeline is considered highly reliable and data is validated easily. The validation includes formatting of files, file sizing, null and invalid data entry. However, in the Machine learning case, the input data is validated using Machine Learning processes. The process is also called cross-validation in machine learning which is done on some amount of data.

 

Hybrid team

MLOps works in coordination and support of data engineers and alone data scientists cannot complete any implementation requirements. Thus it requires the team to work in both cases such as the team handling of MLOps require knowledge of data engineering, DevOps, and ML.

 

Model and data versioning

In software or DevOps, the versioning is flexible in code and it is sufficient and defines all behavior. But in Machine learning, we have to keep model version, data, types of hyperparameters, and their values. However, the MLOps is much more experimental and process-driven. The Developers used different features, parameters, and models to create reproducible results.

 

Performance Degradation

Machine learning scientists are impacted by changing the profiles of data. However, it does not happen in traditional IT systems, the model is refreshed even if it is in working condition causing more iteration in the pipeline.

 

Testing

Testing of Machine learning systems involves checking of prediction metrics, validation of the model, final model training, etc. However, on the side of software, the developers perform tests such as unit and integration testing at the final stage.

 

Difference between MLOps and DevOps

  DevOps MLOps
Main Purpose Automation of  software processes like quality assurance and feedback loops Standardize Machine Learning Lifecycle by processes and automated quality checks
Sub-Process Continuous Integration and Continuous Delivery Continuous Training  and Continuous Validation
Deployment cycles Frequent stepwise iterations Continuous, long training cycles
Team composition Software Development + DevOps + QA Team Machine Learning Engineers + Data Engineers/Scientist
Deliverable Code and integration Model + Training Data + Training parameters
Objectives Business goals Exploration of data and Model Experiments
Quality assurance Code testing Data + Model validation

Conclusion

DevOps and MLOps are discussed in the article in detail. However, there are some similarities but the difference is larger in the process of working of machine learning and software. The place and importance of both MLOps and DevOps are different due to different process requirements.

 

Who do you think will be more important in MLOps teams? DevOps or MLOps? Software Engineer or Machine learning Engineer?

 

Resources:

 

1-MLOps vs. DevOps: the similarities and differences
2-How is MLOps Different From DevOps: A Detailed Comparison
3- MLOps vs. DevOps: What is the Difference?
4- MLOps Vs DevOps: A Comparative Analysis
5- What Is The Difference Between DevOps And MLOps?

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