Docker-Based AI Simulation for Research and Development: Ensuring Deterministic Science

Abstract

This paper explores the use of Docker-based containerization as a foundational approach to achieving reproducibility, determinism, and portability in artificial intelligence (AI) and machine learning (ML) research. Addressing the longstanding reproducibility crisis caused by dependency complexity, environment drift, and inconsistent execution conditions, the study demonstrates how Docker’s isolation, environment parity, and version-controlled images enable consistent replication of computational results across diverse systems. Through an analysis of Dockerfile architecture, best practices, and a complete example workflow for training and deploying a machine learning model, the paper illustrates how containerization provides transparent, traceable, and immutable research environments. The findings show that Docker not only simplifies development workflows but also enhances scientific reliability, accelerates experimentation, and supports collaborative, cross-platform AI development, making it an essential infrastructure for modern deterministic science.