Explore the complexities of implementing a deep learning model for recommendations while maintaining a legacy platform serving millions of customers. Dive into the three-tiered approach for a successful recommendations platform: data, machine learning, and A/B testing. Learn about efficient data collection pipelines, avoiding the "pipeline jungle construct," and holistic data flow management. Discover how to build, train, and evaluate models using the data tier, and understand the importance of A/B testing before exposing algorithms to a large customer base. Compare the legacy platform to the current cloud-based system, examining how these changes improved reliability and stability. Gain insights from Leemay Nassery's experience in resurrecting a recommendations platform while balancing the challenges of limited infrastructure and personnel resources.