This talk will explore the journey of building a scalable, end-to-end recommender system, from data collection to model deployment. We’ll start with the fundamentals of data collection and streaming, emphasizing how to handle real-time data flows using Kafka. Moving into model development, we'll cover essential algorithms and metrics tailored for recommendation systems, alongside best practices for training and evaluation. Beyond model building, we’ll dive into system scalability with load balancing using NGINX, ensuring robust delivery of recommendations under high traffic. Finally, we’ll discuss A/B testing as a validation strategy to measure impact and refine results in production environments. This talk will provide a practical, hands-on roadmap for deploying an end-to-end recommendation engine that meets the demands of modern, high-scale applications. This talk provides practical skills: 1) Full ML pipeline for recommendations, from data collection to deployment. 2) Using Kafka for real-time data streaming, ensuring consistent and reliable data flow. 3) Recommendation algorithms and evaluation metrics essential for model accuracy. 4) Scaling with NGINX for load balancing, to handle high traffic and ensure stability. 5) A/B testing for model validation to improve recommendations and assess real-world impact.