Explore the challenges and solutions for building real-time machine learning pipelines in this 43-minute conference talk. Learn how to handle high-velocity, high-volume data for applications like fraud prediction, predictive maintenance, and customer churn prevention. Discover techniques for online and offline feature engineering, ML calculations in development and production environments, and effective monitoring of AI applications to detect and mitigate drift. Gain insights from real customer case studies and understand how to implement serverless architectures for simplified, high-performance ML pipelines. Delve into topics such as integrated feature stores, Kappa architecture, and MLOps automation to accelerate development and deployment of real-time AI solutions.