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Call For Speakers - Big Data & AI World
Take the Stage at Big Data & AI World Asia 2026
Are you a leader in AI and data technologies? Take the stage at Big Data & AI World Asia 2026 and share real-world insights with an audience of senior decision-makers and practitioners. We are looking for expert speakers with compelling case studies, practical lessons, and proven strategies drawn from real-world applications and production-grade AI and data systems.
Submit your case study for the following areas:
- AI-Ready Data – Building a foundation for ethical and reliable AI
- Responsible AI – Developing effective governance frameworks
- AI Engineering – Delivering scalable, production-grade AI systems
- LLMOps – Deploying, maintaining, and monitoring large language models
What are we looking out for?
The conference team are calling for innovative papers and case studies from CIOs, IT Directors, CEOs, CTOs and other senior technology executives. The team is particularly keen to receive papers from experienced practitioners in fields such as artificial intelligence, data and analytics, governance and more.
The conference will offer different presentation opportunities, designed to address the most pertinent and compelling issues in AI and big data.
Topics for 2026
AI-Ready Data – Building a Foundation for Ethical & Reliable AI
- From Raw to Responsible: Engineering Data Pipelines for AI-Grade Quality
- Data Governance 2.0: Accountability in the Age of Generative & Agentic AI
- Privacy-Preserving Data Architectures for AI Systems
- Context is King: Knowledge Graphs, RAG & Semantic Layers for Reliable AI
- Data Observability: Monitoring Drift, Decay & Distribution Shifts
- Synthetic Data & Simulation: Expanding Training Boundaries Responsibly
Responsible AI – Formulating AI Governance Frameworks
- Global Regulatory Convergence: Navigating Fragmented AI Laws
- From Principles to Practice: Operationalising Responsible AI Across the Enterprise
- AI Risk Management Frameworks: Aligning Innovation with Accountability
- Transparency & Explainability: Making Complex Models Audit-Ready
- Building a Culture of Responsibility: Leadership, Incentives & Ethical Accountability
AI Engineering – Developing Scalable AI Systems
- From Prototype to Production: Engineering Scalable AI Pipelines
- Distributed AI Training: Scaling Models Across Multi-Cloud & Edge
- Model Optimisation: Efficient Inference Without Compromising Accuracy
- AI Reliability & Resilience: Designing Systems for Failure Tolerance
- Feature Engineering & Data Orchestration for Enterprise AI
- Cost-Effective Scaling: Balancing Compute, Storage, and Performance
LLMOps – Deploying, Maintaining & Monitoring LLMs
- Continuous Learning & Fine-Tuning: Maintaining LLM Performance Over Time
- Observability for LLMs: Monitoring Latency, Accuracy & Hallucinations
- Data Pipelines for LLMOps: Ingestion, Cleaning & Context Management
- Cost Optimisation & Scaling Strategies for LLM Deployment
- Security & Compliance in LLMOps
- Versioning & Rollbacks: Managing Multiple LLMs Safely
- Human-in-the-Loop & Feedback Loops for LLM Optimisation
- Measuring LLM Impact: Metrics Beyond Accuracy
Submit Your Case Studies
Deadline: 30th August 2026
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