Implementing Continuous Integration and Deployment (CI/CD) for Machine Learning Models on AWS

This study offers an insight into best practices for continuous integration and deployment of Machine Learning on AWS platforms. CI/CD promises the value of reliability in model deployment, agility, cost optimization, scalability, and efficiency. CI/CD operates on basic principles such as Automation, continuous deployment, Automated Testing, Continuous Monitoring, and Feedback Loop. These principles guide the application, enabling critical handling and modeling of the key inferences to assist in the targeted management of the ML models. Moreover, this article states that the best practices used include containerization for portability, monitoring, and logging, infrastructure as a code, automated testing and validation, and version control. Using these practices helps to leverage the role of CI/CD to enable suitable platforms to address major approaches to handle the required value of achieving suitable outcomes. Thus, the use of CI/CD, should be adopted by companies since it provides the capacity to have reliable model deployment and automation of key functionalities related to the deployment of ML in AWS.