VAR-As-A-Service is an MLOps approach for the unification and reuse of statistical models and machine learning models deployment pipelines. It is the second of a series of articles that is built on top of that project, representing experiments with various statistical and machine learning models, data pipelines implemented using existing DAG tools, and storage services, both cloud-based and alternative on-premises solutions. This article focuses on the model file storage using an approach also applicable and used for machine learning models. The implemented storage is based on MinIO as an AWS S3-compatible object storage service. Furthermore, the article gives an overview of alternative storage solutions and outlines the benefits of object-based storage.

The first article of the series (Time Series Analysis: VARMAX-As-A-Service) compares statistical and machine learning models as being both mathematical models and provides an end-to-end implementation of a VARMAX-based statistical model for macroeconomic forecast using a Python library called statsmodels. The model is deployed as a REST service using Python Flask and Apache web server, packaged in a docker container. The high-level architecture of the application is depicted in the following picture:

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