At Nikon, camera accessories are essential in meeting the diverse needs of professional photographers worldwide, making their timely availability a priority. Forecasting accessories, however, presents unique challenges including dependencies on parent products, sparse demand patterns, and managing predictions for thousands of items across global subsidiaries. To address this, we leveraged Databricks' unified data and AI platform to develop and deploy an automated, scalable solution for accessory sales planning. Our solution employs a hybrid approach that auto-selects best algorithm from a suite of ML and time-series models, incorporating anomaly detection and methods to handle sparse and low-demand scenarios. MLflow is utilized to automate model logging and versioning, enabling efficient management, and scalable deployment. The framework includes data preparation, model selection and training, performance tracking, prediction generation, and output processing for downstream systems.