# Improving Automated Machine-Learning Systems through Green AI
## 2 State of the Art
Additional sources of variation arise from random initialization and dataset diversity, which can impact the learned model parameters and filter responses [52]. In practical terms, the primary objective is to ensure that, despite the inherent variability introduced by these factors, the models effectively capture salient features and maintain consistent performance. A significant area of uncertainty revolves around whether different training methodologies consistently yield VAEs or ConvVAEs models with similar generative capabilities. It is plausible that divergent training processes result in models with distinct filter weight configurations, while these models continue to exhibit comparable performance [52]. Furthermore, these training processes may potentially yield models with differing architectural configurations, involving variations in the number and type of network layers [53]. Consequently, the performance of these models may diverge, especially concerning the complexity of the data they aim to generate [54]. Clearly, these variations in model architectures bear significant implications for ecological sustainability and significantly influence the development of a sustainable paradigm for artificial intelligence. Multi-objective optimization techniques, as surveyed by [PERSON] et al. [55] and explored by [PERSON] et al. [56] in the NEMO framework, offer a promising avenue for explicitly integrating sustainability objectives into the model training process. Specifically, these methods employ evolutionary algorithms to Pareto-optimize models for multiple objectives simultaneously, such as accuracy, latency, and energy efficiency. In a parallel approach, hybrid human-AI approaches, as exemplified by the work of [PERSON] et al. [57], harness human inputs to enhance model efficiency. In particular, these approaches harness human inputs to enhance model efficiency through human-in-the-loop training. This strategy guides autoencoders toward meaningful and resource-efficient representations, mitigating the computational overhead associated with training. The integration of human domain knowledge further contributes to avoiding the inefficiencies inherent in pure black-box training strategies. Quantization, as introduced by [PERSON] et al. [58], emerges as a potent tool for curtailing computational and environmental costs associated with machine-learning models. Essentially, by reducing the number of bits used to represent model parameters and weights, quantization substantially diminishes both model size and energy consumption. Pruning, elucidated by [PERSON] et al. [59], offers another avenue for enhancing efficiency. This involves the removal of unnecessary connections and parameters from a model, leading to reductions in both model size and computational demands. Lastly, knowledge distillation, originally proposed by [PERSON] et al. [60] and further advanced by [PERSON] et al. [61], involves the transfer of knowledge from a large teacher network into a smaller student network. This process not only minimizes model complexity but also enhances the sustainability of machine-learning systems by reducing computational demands. Collectively, the pursuit of sustainable machine learning involves a comprehensive array of strategies spanning efficient model design, hyperparameter optimization, lightweight models, multi-objective optimization, and hybrid human-AI approaches. These strategies form a cohesive approach to not only improving the environmental sustainability of AI but also maintaining and even enhancing model performance. Overall, promising strides are being made towards sustainable AI. The research discussed in this section highlights some of the most promising approaches to balancing machine-learning performance with sustainability considerations. These solutions encompass a diverse range of methodologies, primarily deployed within the domain of deep learning. In contrast, our proposition centers on tabular data and AutoML solutions utilizing conventional algorithms.