This paper investigates the viability and efficiency of parameter search for machine learning (ML) pipelines through containerization in diverse computational environments. We leverage parallelization by executing each test in the grid search as an independent container, allowing for scalable and efficient parameter tuning. Our study encompasses a detailed comparison of energy efficiency, power consumption, and execution times between two distinct setups. The first setup comprises a cluster of eight Raspberry Pi Compute Module 4 units integrated into two Turing Pi boards, each housing four nodes, all managed within a single Kubernetes cluster. This configuration showcases the potential of cost-effective, low-power devices in executing complex ML workflows. The second setup involves a traditional high-performance computing environment with multi-core CPUs and GPUs, where some parameter tests are offloaded to the CPU and others to the GPU, enabling a form of hybrid computing. By juxtaposing these two configurations, we aim to highlight the practical implications of containerized hybrid parallelism, particularly in terms of resource utilization and performance metrics. Our findings reveal insights into the trade-offs and benefits of using lightweight, distributed clusters versus more conventional, centralized computing resources for ML parameter optimization. This study underscores the potential of containerization in enhancing the flexibility and efficiency of ML workflows across varying hardware architectures.