Hands-on Scalable Edge to Core ML Pipelines

This intensive workshop focuses on designing, implementing, and troubleshooting real-world distributed ML pipelines from edge devices to core infrastructure. Key MLOps challenges for building and managing scalable systems for operational analytics and AI/ML workflows are addressed. Topics include edge computing for data ingestion, streaming architecture with open-source tools, distributed processing across heterogeneous environments, model deployment strategies, and observability/monitoring for distributed ML systems. Participants engage in hands-on activities to build a complete edge-to-core ML pipeline, deploy models for real-time inference, and practice data validation and troubleshooting.