The AI infrastructure Track spotlights the foundational systems that enable scalable, secure, and high-performance AI across drug discovery and development. As AI applications become more advanced, robust infrastructure is key to enabling seamless data integration, secure collaboration, and effective model deployment. This year’s program explores the evolving pillars of AI infrastructure, from next-gen data systems to DevOps and ML Ops, reflecting the growing complexity of real-world AI implementation.
Designed for infrastructure leads, CIOs, cloud architects, ML engineers, and digital innovation leaders in pharma, biotech, and healthcare, this track offers actionable insights for building and scaling AI-driven drug discovery systems.
Build Scalable AI Infra from Day One
Discover best practices for architecting infrastructure that can handle high-dimensional healthcare data, power predictive modelling, and support cross-functional collaboration - whether in the cloud, on-premise, or hybrid environments.
Accelerate Innovation Through ML Ops
Learn how ML Ops integration drives operational efficiency, enabling continuous deployment of AI models and tighter alignment between data science and engineering teams to streamline R&D workflows.
Ensure Security, Governance & Ethical AI at Scale
Explore how to implement robust data governance frameworks and cybersecurity protocols that safeguard sensitive research data while ensuring compliance and ethical use of AI across global organizations.
Agenda Highlights
- Cloud vs. On-Premise Platforms: Compare infrastructure strategies that balance cost, scalability, compliance, and performance to best support AI workloads in early-stage research.
- Scaling with High-Dimensional Data: Explore how to extract actionable insights from complex multi-modal healthcare data, fuelling integrated analytics and strengthening your competitive edge in precision medicine.
- ML Ops from Molecule to the Market: Dive into how ML Ops frameworks are transforming R&D by enabling rapid model iteration, reproducibility, and continuous delivery in regulated environments.
- Data Governance & Ethical AI: Understand how to build and operationalize data governance strategies that ensure responsible AI development and align with emerging regulatory standards.
- Open Innovation & Cross-Sector Collaboration: Examine models for collaboration between pharma, tech companies, and research institutions to co-develop the next generation of AI platforms and standards.