Platform-First Forecasting - Engineering Scalable ML Systems

Learn to build scalable machine learning forecasting systems through a platform-first engineering approach in this 24-minute conference talk from Conf42 Platform Engineering 2025. Discover how to rethink traditional ML-based forecasting by prioritizing platform architecture and engineering principles over pure model accuracy. Explore the key components that make forecasting platforms successful, including comprehensive success metrics that extend beyond simple accuracy measurements. Master asymmetric error handling techniques specifically designed for forecasting scenarios where different types of prediction errors carry varying business impacts. Examine microservices architecture patterns optimized for ML systems, along with containerization and orchestration strategies that ensure reliable deployment and scaling. Understand API design best practices that enable seamless integration of forecasting capabilities into broader business systems. Gain insights from real-world implementation success stories that demonstrate practical applications of these concepts. The presentation concludes with future directions in platform-first ML engineering, providing a roadmap for evolving forecasting systems to meet growing business demands and technical requirements.