In the dynamic world of competitive gaming, delivering personalised user experiences and driving meaningful business outcomes requires an advanced machine learning (ML) platform. Join this session to explore the architecture and implementation of a scalable ML ecosystem designed to enable real-time feature engineering, inference, and personalisation at scale , using the power of Flink and Kafka combined while the duo of BigTable & Redis acting as feature storage. We will dive into the ML Platform Architecture following the key points: Realtime Personalisation: Realtime Decision and Recommendation to influence user [viz. Adapting game lobbies, missions, and rewards to user preferences, with a turn around time of less than a few seconds] Feature Store: The heart of ML platform, which automates realtime feature preparation & processing pipelines. It also acts as low latency and high frequency store for realtime raw data, aggregate calculations, transformation to provision/build/create/serve features Observability: System observability; Tracking & monitoring ML experiments, DQA [Data quality assessment] Business Impact: Personalised User Journey (New, Old), Game and lobby Recommendation, Missions, Rewards (Offers, Coupons) boost the user retention & higher user engagement. Skill Benchmarking, gives user a fair opportunity to participate in game with a opponent , which increase the user engagement & clock time of the user spent on the platform It helps decide & draw the user graduation journey in the platform, which allows for opportunities to cross sell & up sell in the platform