Overview Nomova.ai is an AI/ML-powered vacation planning platform designed to generate personalized travel recommendations using predictive modeling and cloud infrastructure. The goal of the project was to explore how modern AI systems can be built end-to-end—from model development to production deployment—while keeping the system scalable, maintainable, and practical in a real-world environment. Problem Travel planning is typically fragmented across multiple platforms. Users jump between search engines, booking sites, and review platforms, then manually combine information into a decision. This creates friction in three key ways: High cognitive load Nomova.ai was built to address this by consolidating the experience into a single, AI-driven recommendation system. System Design The system was designed as a cloud-native SaaS architecture with a clear separation between data processing, machine learning, and serving infrastructure. Machine Learning Layer The core predictive functionality was built using PyTorch, with models deployed through Vertex AI. This layer handles: Learning user preference patterns The model design focuses on combining user signals with contextual inputs to produce ranked outputs. Feature Processing Layer Raw user interaction data is transformed into structured signals before being passed into the model. Key feature groups include: Behavioral interaction history This separation ensures the model remains decoupled from raw input complexity and improves maintainability. Cloud Deployment The system is deployed using Vertex AI for model serving and infrastructure management. This setup enables: Scalable inference endpoints This allowed the system to move from experimental development into a production-ready environment without restructuring core logic. Engineering Decisions The ML layer, feature pipeline, and serving infrastructure were intentionally separated to allow independent development and iteration. Cloud-Native Architecture Using Vertex AI reduced operational overhead and allowed the system to scale inference workloads without manual infrastructure management. Iterative Model Development The system was designed to support experimentation, allowing models to be updated and evaluated without disrupting production workflows. Challenges New users lack historical interaction data, requiring fallback logic to generate meaningful initial recommendations. Model Generalization Balancing personalization with general travel relevance required careful feature selection and tuning. Production Transition Moving from local development to a deployed system required restructuring inference flows and ensuring consistency between environments. Outcome Nomova.ai was successfully delivered as a production-ready system and handed off following completion of its core machine learning and infrastructure components. The project demonstrates experience in: Designing scalable ML systems Building cloud-native architectures Moving models from development to production Structuring systems for long-term maintainability Stack PyTorch Google Vertex AI Google Cloud Platform Python-based ML pipelines