Cloud Infrastructure Melbourne 2025 schedule

In this 5-minute networking session, the goal is to connect with three new people. Enjoy the opportunity to expand your network!  

• How quantum computing, edge AI, and serverless architectures are reshaping IT infrastructure.
• Strategies to maximize ROI while maintaining flexibility and scalability in evolving cloud environments.
• Aligning cloud adoption with long-term business goals to stay ahead in an AI-driven landscape.

Panellists

Harpreet Chopra Head of Technology (Cloud) NAB

Chandima Miyanadeniya Global Head of Enterprise Architecture Kennedys

Rajesh Padmawar EM – Architecture The Salvation Army Australia

• Overcoming common challenges in cloud migration, from legacy system integration to workload optimization.
• Key considerations for selecting the right cloud model—public, private, hybrid, or multi-cloud.
• Best practices to ensure minimal downtime, cost efficiency, and security during migration.

• The evolution of FinOps and its role in managing cloud costs effectively.
• Key cost optimization strategies enterprises are implementing today.
• Challenges and trends in balancing cloud adoption, security, and ROI.

• Optimising cloud compute resources for high-performance workloads, including GPU, CPU, and accelerators.
• Designing scalable storage architectures to balance performance, cost, and data accessibility.
• Enhancing cloud networking with low-latency, high-bandwidth connectivity for distributed workloads.

• Understanding Distributed Cloud: How it differs from hybrid and multi-cloud models and its impact on enterprise IT strategies.
• Reducing latency, improving data sovereignty, and ensuring high availability across distributed environments.
• Best practices for adopting distributed cloud in AI workloads, edge computing, and complex enterprise architectures.

Co-presenters:

Sakshee Koli Head of Engineering & Site Infrastructure Coles Group

Rochana Attale Enterprise Architect Coles Group

• The evolving role of hyperscalers, colocation providers, and managed services.
• How enterprises can optimise cloud partnerships for scalability and security.
• Achieving cost efficiency while ensuring high performance.

• The shift from traditional IT to AI-optimised cloud infrastructure.
• Balancing cost, performance, and scalability in AI-first environments.
• What enterprises must do today to stay competitive in the cloud era.

• How enterprises are modernising cloud infrastructure while ensuring security.
• Overcoming the complexity of multi-cloud and hybrid environments.
• Addressing cybersecurity risks in AI-powered cloud systems.

• How enterprises can move away from rigid infrastructure models to more flexible, scalable, and cost-efficient cloud adoption.
• Strategies for giving teams greater autonomy while ensuring security, compliance, and operational control.
• How organizations can reduce cloud spending, manage FinOps effectively, and avoid overprovisioning while scaling.

• Overcoming latency and performance bottlenecks in hybrid cloud.
• Building cloud environments that dynamically scale with business needs.
• Aligning IT infrastructure with AI and data-intensive workloads.

• Why traditional data centers are rigid, costly, and insecure.
• How a modular, service-driven cloud model enhances agility and scalability.
• Steps to transition from on-prem to a modern cloud ecosystem without compromising security or performance.

• Implementing Zero-Trust principles in cloud-native architectures to minimise attack surfaces.
• Identity and access management (IAM) strategies to secure workloads and prevent unauthorised access.
• Network segmentation and microservices security for reducing lateral movement threats in cloud environments.

• What enterprises need before scaling AI workloads
• When to use cloud, hybrid, or on-prem for AI-driven applications.
• Managing risk and cost while ensuring AI performance at scale.

• Protecting cloud-native AI applications from emerging cyber threats.
• The role of zero-trust architecture in securing AI-driven cloud systems.
• Strategies to ensure resilience against AI-powered cyberattacks.

• How composable architecture enables flexible, API-driven infrastructure for AI.
• Optimising AI workloads using microservices, Kubernetes, and Infrastructure as Code (IaC) for dynamic resource allocation.
• Ensuring interoperability across multi-cloud, edge, and AI ecosystems.

• Designing cloud-native data architectures to support real-time analytics and AI-driven insights.
• Data governance and compliance considerations when managing AI data in hybrid and multi-cloud environments.
• Leveraging serverless and containerised solutions to automate and optimise AI data processing at scale.

• An introduction to effective Shared Responsibility Models that keep platform teams focussed on their priorities while ensuring app teams maintain compliance.
• How to establish tight feedback cycles, automated compliance monitoring, and clear accountability for security posture and audits.
• Foundational strategies for moving quickly in the cloud while maintaining visibility and governance.

• Cybersecurity threats facing AI and cloud-powered enterprises.
• Balancing AI innovation with security, compliance, and governance.
• Future-proofing enterprise cloud environments against evolving risks.

Panellists
Jan Cutts EM, Cloud & Hybrid Infrastructure nbn Australia

In the highly competitive age of digital transformation financial service organizations are facing accelerated urgency to improve their customer and employee experience while simultaneously reducing operating costs, and managing risk and compliance.

To meet these competing demands on their business, these organizations are racing to deploy deep learning to achieve a new competitive edge by optimizing their back office operations with intelligent document processing, personalizing their customer experience with cutting edge NLP models, and reducing fraud and risk using state-of-the-art deep learning.

AI is here and delivering new capabilities to help businesses solve large and complicated challenges. Join Bob Gaines to learn what that means for your business and how deep learning is helping organizations:

• Achieve higher compliance, faster and with lower costs • Dramatically improve Customer Experience • Reduce time to value from years to weeks

Sergio Rego is a customer engineer at SambaNova Systems where he helps clients deploy purpose-built, deep learning solutions in weeks rather than years. Sergio started his career in financial services, where he worked in strategy; active and index management; and product design and management. Sergio also served as a senior data scientist and team manager for a system integrator where he helped federal government agencies deploy ML and AI solutions.