Accelerating the Machine Learning Model Deployment using MLOps

Abstract

Machine Learning projects acquire a large amount of data to make predictions on the data. Deploying machine learning models is a difficult task as it is involved a lot factors such as continuous builds to improve efficiency and different libraries used for prediction. As the data grow for better predictions with proper accuracy some of the parameters are to be dynamically changed for the sake of performance tuning. To enhance the variation in the parameters it takes effort and time from the developers to make the changes in the code and deploy to the production environment. In proposed work, an end to end automation cycle to enhance the timely deployment of machine learning models with better performance is presented. This automation cycle converts the changed code with varied parameters into the container image and uploads to the container registry. Later, when the new image is uploaded it auto detects and updates the production environment with the new image data by launching a new container and shutting down the previous old one. The proposed automated model will display the updated features which are developed. The users can utilize the updated model simultaneously without any downtime to obtain the new features. The main contribution of proposed work is that it can build and deploy the machine learning models automatically, without any manual intervention. The models can also be retrained for better performance of the predictions.