An exploration of the complexities involved in engineering scalable AI systems, including data pipelines, model deployment, and real-time processing.

Discussing the latest advancements in hardware and cloud infrastructure that enable high-performance AI computations.

An interactive session where attendees collaborate to deploy a machine learning model using containerization and orchestration tools, focusing on scalability and reliability.

A case study on designing efficient data pipelines to handle large-scale data ingestion, processing, and storage for AI applications.

Practical insights into integrating CI/CD practices tailored for machine learning models to maintain performance and accuracy in production environments.

Exploring the opportunities and challenges of deploying AI models on edge devices for low-latency and offline capabilities.

Delegates will chose from a list of pertinent peer-to-peer discussion topics focussing on evolving and emerging trends, techniques and technologies

Insights into incorporating ethical considerations into AI engineering practices, including bias mitigation, transparency, and accountability.

A lively debate on whether cloud-based or on-premises infrastructure offers the optimal environment for AI development and deployment.