Building Developer-Centric ML Inference Platforms

Explore the comprehensive architecture and implementation strategies for building developer-centric machine learning inference platforms in this 28-minute conference talk from Conf42 Platform Engineering 2025. Discover the key challenges faced when constructing ML inference platforms and learn how to establish solid architectural foundations that support scalable machine learning operations. Dive deep into leveraging Kubernetes and Custom Resource Definitions (CRDs) for ML workflows, implementing automated CI/CD pipelines specifically designed for machine learning projects, and designing robust feature store and data pipeline architectures. Master various model serving strategies and understand how to implement effective monitoring and observability solutions for ML systems. Gain insights into fostering organizational excellence and team collaboration in ML platform development, while learning to address scaling challenges and optimization strategies for high-performance inference systems. Examine critical aspects of security, compliance, and governance in ML platforms, and explore emerging trends that will shape the future of ML inference platforms. Conclude with actionable insights and best practices for building developer-friendly ML infrastructure that enables teams to deploy and manage machine learning models efficiently at scale.