What Is GitOps and How Can It Support Machine Learning Operations? – DATAVERSITY

GitOps is a way of implementing continuous delivery for cloud native applications. It is based on the idea of using Git as a single source of truth for declarative infrastructure and applications.

In GitOps, the desired state of the infrastructure and applications is stored in version control, and an automated process is used to ensure that the actual state of the system always matches the desired state. This can be achieved by using tools such as Kubernetes and Argo CD to monitor the Git repository and apply any necessary changes to the system. 

By storing the desired state of the system in Git and using automated processes to ensure that the actual state matches the desired state, GitOps can reduce the risk of errors that can be introduced when manually updating systems.

Git is a widely used tool for collaboration, and using Git as the source of truth for infrastructure and applications makes it easy for teams to collaborate and make changes to the system. Git stores a history of all changes made to the system, making it easy to track changes and roll back if necessary. This can be useful for auditing and compliance purposes.

GitOps and CI/CD: Hand in Hand

CI/CD (continuous integration/continuous delivery) is a software development practice that aims to minimize the time between writing code and delivering it to users by automatically building, testing, and deploying code changes.

The “continuous integration” part of CI/CD refers to the practice of regularly integrating code changes into a shared code repository, and the “continuous delivery” part refers to the practice of automatically building, testing, and deploying code changes.

CI/CD helps to ensure that code is always in a deployable state, and it can significantly speed up the software delivery process. It is a key component of agile software development, and organizations of all sizes are increasingly adopting it.

GitOps and CI/CD complement each other because GitOps provides a way to automate the deployment of code changes, while CI/CD provides a way to automatically build and test code changes. By using GitOps and CI/CD together, organizations can significantly improve the speed and reliability of their software delivery process and reduce the risk of errors.

For example, in a GitOps workflow, code changes are committed to a Git repository, and the GitOps system automatically deploys those changes to the appropriate environments (e.g., staging or production). The CI/CD system can then be used to automatically build and test the code changes, ensuring that they are working as expected before they are deployed to users.

MLOps and GitOps 

MLOps, or machine learning operations, is a set of practices and tools that allow organizations to effectively develop, deploy, and maintain machine learning models in a production environment. It involves the collaboration of data scientists, engineers, and IT professionals to build and operate a robust and scalable machine learning infrastructure.

MLOps and GitOps share some similarities in that they both focus on automating and streamlining the development and deployment process. However, MLOps specifically deals with the operational aspects of machine learning, while GitOps is more broadly applicable to the continuous delivery of any type of cloud native application.

How Does GitOps Benefit AI Development and MLOps?

GitOps can benefit AI development and MLOps in several ways.

Governance

By storing the desired state of the system in Git and using automated processes to ensure that the actual state matches the desired state, GitOps can help to improve governance and control over AI and machine learning systems. This can be particularly important in regulated industries where it is important to track and understand changes to the system.

Developer Lock-In

Developer lock-in is a term used to describe the dependence of a system on specific individuals or teams of developers. It can occur when a system is designed and implemented in such a way that it is difficult or impossible for other developers to understand or make changes to it without the help of the original developers.

GitOps can help to reduce developer lock-in by making it easier for different teams to collaborate and work on AI and machine learning systems. By using Git as the source of truth for the system, it is easier for developers to understand how the system works and to make changes without being dependent on specific individuals or teams.

Reproducible Experiments

GitOps can also help to improve reproducibility in machine learning experiments by storing the configuration and dependencies for experiments in Git. This makes it easier to recreate experiments and to understand how changes to the system might impact the results.

Retesting

By storing the configuration and dependencies for machine learning models in Git, GitOps can make it easier to retest old models and compare the results to newer versions. This can be useful for understanding how models have changed over time and for identifying any issues or problems.

Switching Environments

GitOps is declarative and can make it easier to move machine learning models between different infrastructure environments (e.g., from a development environment to production). By storing the desired state of the system in Git, it is easier to understand the dependencies and configuration needed to run the models, and to automate the process of deploying them to different environments.

Conclusion

In conclusion, GitOps is a practice that aims to improve the continuous delivery of cloud-native applications by using Git as a single source of truth for declarative infrastructure and applications. It involves the use of automated processes to ensure that the actual state of the system always matches the desired state, which is stored in version control. GitOps has several benefits, including reduced risk of errors, improved collaboration, and auditability.

GitOps can also be used to support machine learning operations (MLOps) by providing a way to automate the deployment of machine learning models and to improve collaboration between data scientists, machine learning engineers, software developers, and operations teams. 

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