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 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?
Continuous Integration (CI)
Continuous 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 as machine learning operations, are the practices and processes which are used to streamline the machine learning cycle from start to end.
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 model.
MLOps and ML model management involve development and operations, but they are both separate and combined later on.
MLOps gives some type of standard for data collection, preprocessing, training of model, model deployment and then real time operation of model. MLOps 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.
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 on the basis of best performance and analysis metrics.
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.
Data pipelines are defined as the sequence of transformations which occur in data 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 CI/CD pipeline
Using DevOps approach the collection of monitoring data is important before start 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 ML case the prediction performance is checked only with some features
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.
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.
Machine learning scientists are impacted by changing 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 of Machine learning systems involves checking of prediction metrics, validation of 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.