# Data-Driven Decision Support in SaaS Cloud-Based Service Models
## 5 Discussion
### Selection of Optimal Machine Learning Model for Decision-Making
Even though carbon footprint should be taken into consideration, only two studies [13; 15] measured how machine learning model selection affects carbon emissions. Study [15] examined the environmental impact of machine learning in B2B churn predic  
Figure 4: Taxonomy for ML algorithms selection for SaaS providers. tion, analyzing the trade-offs between model accuracy and carbon emissions. Using an AMD EPYC 7763 processor (280 W) and European emissions data (296.96 g CO\({}_{2}\)/kWh), the research quantified CO\({}_{2}\)eq emissions in relatable terms. The results revealed that even basic preprocessing, such as feature creation, emits 86.379 g CO\({}_{2}\)eq, while model training introduced far greater costs. The inclusion of multiple usage features resulting in a high dimensional dataset, significantly affected the carbon footprint produced by the reviewed machine learning methods. Specifically, Decision Trees and Random Forests showed a \(\sim\)200% rise in emissions (DT: 2.349 g CO\({}_{2}\)eq to 4.044 g; RF: 1.350 g to 4.044 g). XGBoost's emissions rose by 335% (21.29 g CO\({}_{2}\)eq), and SVM increased by 50% (40.99 g CO\({}_{2}\)eq), whereas Logistic Regression remained efficient (2.349 g CO\({}_{2}\)eq). However, the most striking environmental costs seemed to derive from deep learning and complex ensemble methods. In study [13], a Deep Neural Network (DNN) with two hidden layers (32/64 neurons) achieved a high accuracy percentage but at a high computational expense, scaling considerably as model complexion increased. Similarly, Random Forest proved resource-intensive, with costs growing alongside tree depth and feature complexity. Both DNN and RF required parallel computation to train efficiently and used up all of the available processors indicating their high resource consumption in comparison with the rest of the reviewed models. Although advanced models seemed to improve prediction, their energy demands make them unsustainable for firms without optimized infrastructure. The study concluded that businesses, especially smaller enterprises, should prioritize energy-efficient algorithms (like Logistic Regression) or cloud-based solutions to reduce environmental impact. Although several of the reviewed studies experimented with deployment aspects, such as standalone real-time applications [5], CRM-integrated dashboards for decision support [14], cloud-based prediction APIs [29], and real-time data streaming using platforms like AWS SageMaker [20], these efforts primarily emphasize centralized, cloud-centric architectures. An emerging direction for real-world deployment of decision support systems in SaaS would involve combining centralized model training with decentralized inference through edge computing [59], particularly in settings involving IoT devices or local clients. Following this, machine learning models are trained in the cloud, often via MLaaS platforms that provide scalable computing resources, versioning and monitoring, and then deployed to edge devices for local inference. This approach could potentially offer key benefits such as reduced latency, improved bandwidth efficiency, offline functionality, and enhanced privacy, as predictions can be made without transmitting sensitive data back to the cloud.