Sydney AI Engineering & Infrastructure Summit 2025

Discover how Lendi Group is revolutionising the Australian home loan industry through cutting-edge AI infrastructure. This session offers an in-depth look at the technologies and strategies powering their AI-driven solutions, from data pipelines to agentic automation, all designed to enhance broker efficiency and customer experience.‍

• Architecting scalable AI infrastructure to support enterprise-level applications
• Implementing agentic AI automation to streamline mortgage broking processes
• Leveraging modern data stacks and cloud services to optimise performance and scalability

Behind every ambitious AI roadmap is a set of very real technical and infrastructure constraints. In this fireside chat, an AI innovator and a data centre expert unpack the realities of scaling compute-intensive programs—from planning for GPU demand and density, to selecting infrastructure partners who can flex with your growth. It’s a conversation about making smart bets on the technology backbone of AI success—without getting bogged down in vendor pitches or future hypotheticals.

• What technical growing pains do AI teams hit first when their compute needs spike?
• How should teams plan ahead for GPU demand, model complexity, and infrastructure bottlenecks?
• What infrastructure choices matter most when experimentation speed is as important as scalability?
• How do you assess whether to scale in the cloud, on-prem, or through hybrid partnerships?
• What’s the smartest way to balance performance, cost, and control when selecting infrastructure partners?

This panel explores why AI is becoming a must-have in business and tech modernisation, how to get started, and who needs to be involved to make it work.

• What’s driving businesses to update systems and tools
• How AI can support both the journey and the end result of modernisation
• Who across the business needs to be involved to ensure success
• Practical ways to improve systems and data to make AI work at scale
• Building the right culture, processes, and accountability to keep AI efforts on track

In this innovative session, attendees will be faced with a series of scenarios that they may face in their roles. Attendees will discuss the possible courses of action with their peers to consider the ramifications of each option before logging their own course of action.

Results will be tallied and analysed by our session facilitator and results will impact the way the group moves through the activity.

Will we collectively choose the right course of action?

With hundreds of models running in production and new opportunities emerging through GenAI, Commonwealth Bank of Australia is evolving its approach to Machine Learning Operations (MLOps) to meet both technical complexity and business ambition. In this session, the Chapter Lead for Data & AI will share practical insights into CBA’s MLOps journey—covering feature development, deployment practices, and how the bank ensures its models are delivering measurable value for customers.

• How CBA develops and manages reusable features to support scalable machine learning
• Best practices for deploying, monitoring, and retraining models in production environments
• Practical lessons from early GenAI implementations, and where it fits in the broader ML pipeline
• How MLOps is driving meaningful outcomes for customers—improving service, experience, and trust

Creating AI-powered products and AI-assisted modernisation demand innovation—but too often, AI initiatives stall because teams and organisations struggle to manage the uncertainty that is inherent when innovating with AI and machine learning.

In this session, we’ll share how we bring clarity and momentum to AI projects by:

• Managing AI initiatives like R&D projects, grounded in data science principles
  
• Accelerating test-and-learn cycles with practical, adaptive strategies
  
• Using decision-making frameworks that align investment with confidence levels
  

By approaching AI as a continuous, learning-driven journey, we help organisations reduce risk, increase speed to value, and move from experimentation to impact.

‍

‍

Explore the technical intricacies of designing, deploying, and scaling AI infrastructure. Delve into the tools, frameworks, and architectures that power high-performance AI solutions, and learn how to balance agility, security, and cost-efficiency.

• How do teams architect resilient, high-performance computing environments to support AI workloads at scale?
• How can teams ensure real-time, high-volume data flow for AI?
• Which pipelines streamline model development, deployment, and continuous monitoring?

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

Topics include...

1. Designing Adaptive Systems for Continuous Modernisation
2. Leveraging Open-Source Tools for AI Infrastructure
3. Securing AI Applications Against Emerging Threat

...and more soon to be annoucned!

How do you architect an AI-native platform purpose-built for vector search, LLM workflows, and scale? In this technical session, Relevance.ai Co-Founder Daniel Palmer shares the foundational decisions and cutting-edge infrastructure behind the platform's rapid growth. Gain insights into the real engineering behind enabling powerful, scalable, and enterprise-ready AI capabilities.

• Designing for Scale from Day One: How Relevance.ai architected a vector-first platform to support fast, scalable AI-driven use cases
• Compute and Cost Efficiency at Scale: Balancing performance, latency, and cost using smart infrastructure strategies across multi-cloud environments
• LLM Workflow Orchestration: Powering custom enterprise AI workflows with modular, flexible pipelines and real-time data processing
• From MVP to Enterprise-Ready: Evolving the platform to meet enterprise security, reliability, and integration requirements without sacrificing speed

Join an interactive discussion where you’ll vote, debate, and dissect five of the thorniest challenges facing AI engineering today. Each topic will be explored through live multiple-choice questions designed to split the room and ignite fresh thinking across tech, data, and business leaders.

1. What's the biggest blocker to scaling AI across the enterprise?
2. Who should own AI infrastructure decisions in an organisation?
3. What’s the smartest way to modernise legacy systems for AI readiness?
4. How much automation is too much in AI development?
5. What matters most for long-term AI success?

‍