This approach automates building, storing, and deploying predictive models using a Remote Machine Learning Data Store hosted on Redis, leveraging DevOps CI/CD artifact pipelines to manage machine learning model artifacts across a team, allowing for efficient management of model deployments in intelligent environments. The workflow utilizes an API that can scale out expensive tasks, manages machine learning models with Redis caching, and natively archives models in S3, enabling organizations to focus on improving model predictive accuracy, dataset features, and deploying pre-trained models to new environments for automation and real-time predictions.