< session />

Engineering Excellence: Platforms and Architectures for Real-Time AI Analytics

AI-driven real-time analytics is revolutionizing how organizations process data, derive insights, and make strategic decisions. However, unlocking its full potential requires a strong architectural foundation, platform engineering best practices, and alignment with key non-functional requirements (NFRs). This session explores how AI, scalable architectures, and robust platforms work together to deliver high-performance, real-time AI analytics that drive business impact.

We will begin by examining the role of microservices, event-driven architectures, and data streaming platforms in building AI-powered systems that process data at scale. We will then dive into the foundational aspects of platform engineering, including infrastructure automation, Kubernetes orchestration, and scalable data pipelines, which enable enterprises to operationalize AI models with reliability and efficiency.

Next, we will explore how to optimize AI performance through architectural choices such as serverless AI, vector databases for embeddings, and real-time data enrichment. We will also highlight the importance of security, observability, and compliance in AI-driven ecosystems, ensuring that organizations can trust and scale their AI workloads responsibly.

The session concludes with a look at emerging trends in AI-driven architectures, such as self-optimizing platforms, intelligent observability, and AI-powered autonomous systems, which are reshaping the landscape of real-time data processing.

Key Takeaways

• Architectural Foundations – How microservices, event-driven design, and data streaming power real-time AI.
• Platform Engineering for AI – Leveraging Kubernetes, Terraform, and Infrastructure as Code (IaC) to optimize scalability and deployment.
• Optimizing AI for Real-Time Analytics – Techniques for improving inference speed, managing vector databases, and ensuring high availability.
• Aligning with Non-Functional Requirements (NFRs) – Addressing scalability, security, reliability, and observability in AI-driven platforms.
• Future Trends – The rise of self-optimizing AI architectures and the role of autonomous systems in real-time analytics.

Target Audience

• Software Architects & AI Engineers designing scalable AI-powered platforms.
• Data & Platform Engineers working with Kubernetes, data streaming, and real-time AI workloads.
• Enterprise AI & Cloud Leaders looking to integrate AI into business operations with a robust infrastructure strategy.
• AI/ML Practitioners & Product Teams exploring best practices for real-time analytics and decision intelligence.