Author: Daniela Kolarova

Building complex container-based architectures is not very different from programming in terms of applying design best practices and principles. The goal of this article is to present three popular extensibility architectural patterns from a developer's perspective using well-known programming principles.

Let's start with the Single Responsibility Principle. According to R. Martin, "A class should have only one reason to change." But classes are abstractions used to simplify real-world problems and represent software components. Hence, a component should have only one reason to change over time. Software services and microservices in particular are also components (runtime components) and should have only one reason to change. Microservices are supposed to be a single deployable unit, meaning they are deployed independently of other components and can have as many instances as needed.

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:

VARMAX-As-A-Service is an MLOps approach for the unification and reuse of statistical models and machine learning models deployment pipelines. It is the first of a series of articles that will be 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. 

But what is VARMAX and statistical models in general and how are they different from machine learning models?