Melbourne AI Engineering & Infrastructure Summit 2027

Beat the rush and join us early for complimentary barista-made coffee and breakfast.

Most enterprise AI is now live, but production is exposing the infrastructure and architecture gaps prototypes never had to face.

This keynote frames what it takes to run AI at enterprise scale, reliably, in systems that can't afford downtime.

• Why scaling AI is now an infrastructure problem, not a model problem
• What breaks first when AI moves into critical, always-on systems
• The architecture decisions that determine whether AI scales or stalls

As Australia's AI governance expectations tighten without a single AI Act, responsible AI has become a moving target for boards and technical teams alike.

This session covers what's actually required now and how to build governance that keeps pace with the rules.

• What regulators expect from AI governance today, in practice
• Building a responsible AI framework that adapts as rules change
• Where governance ownership should sit between technical and executive teams

As AI agents start talking to other AI agents without a human in the loop, traditional access controls don't hold up.

This panel covers how leading teams are applying zero-trust principles to agent-to-agent communication.

• Why agent-to-agent interactions need their own trust model
• Authentication and authorisation approaches built for autonomous agents
• Where zero-trust architecture for agentic AI is still immature

Moving an AI model from a successful pilot to full production can uncover challenges that simply don't appear during testing. As usage grows, teams need to manage performance, reliability, infrastructure, and deployment without disrupting the wider business.

This case study explores how one team scaled an AI model into production, the problems they encountered along the way, and the lessons they learned from the experience.

• The performance and scaling bottlenecks that only emerged at production volume
• How the team approached model versioning, rollback, and safer deployments
• What they learned from the process and what they would do differently next time

A hands-on, interactive session working through a real production AI scenario as a room. Details announced soon.

AI models are only as effective as the data that supports them. Slow, unreliable, or poorly designed data pipelines can impact model performance and make it difficult to scale AI successfully.

This case study explores how one team redesigned its data pipeline to improve speed, reliability, and model performance, while keeping existing systems running throughout the transition.

• How they identified data pipeline issues that were affecting model quality and performance
• How they redesigned the pipeline to reduce delays and improve data reliability
• How they managed the transition without disrupting existing AI workloads

Moving agentic AI from a small pilot into core business functions brings new challenges around scale, security, and governance. These challenges become even more important when AI agents begin working with sensitive financial, employee, and business data.

This case study explores how one organisation expanded agentic AI workflows across finance, HR, and other business functions, while putting the right controls in place and demonstrating measurable value.

• How they designed agentic workflows that could scale beyond the initial use case
• What guardrails and controls they introduced when agents began accessing sensitive business data
• The measurable impact on productivity, efficiency, and business outcomes after rollout

For organisations in regulated industries, adopting AI requires more than policies and good intentions. Teams need practical technical controls that allow AI to operate safely, reliably, and within regulatory requirements.

This panel explores how organisations are building guardrails into AI systems, testing them before deployment, and balancing innovation with the need for security, compliance, and oversight.

• How to design practical AI guardrails that meet regulatory and compliance requirements
• How teams are testing and validating AI before it is used in sensitive or regulated processes
• Where technical controls are essential and policy alone is no longer enough

Small-group, discussion-based sessions where you'll work through real production AI challenges with peers in similar roles. Roundtable topics will be announced soon.

Put your knowledge to the test in this fast-paced quiz covering real-world trivia, key concepts, and emerging trends. Compete for bragging rights — and a voucher — as the top scorer takes the crown.

AI models can change in performance over time as customer behaviour, data, and business conditions evolve. Without the right monitoring in place, these changes can go unnoticed until they begin affecting customers, operations, or revenue.

This case study explores how one team discovered a model drift issue that nearly became a significant business problem, and how they built better observability into their AI pipeline to identify and respond to issues earlier.

• How a model drift incident exposed gaps in their monitoring and almost resulted in a measurable business impact
• How they introduced the right metrics, alerts, and performance thresholds to identify model drift before it became a bigger problem
• How improved AI observability helped the team identify and respond to issues in days rather than weeks

AI adoption is growing quickly, but proving its business value remains a challenge. Moving from a successful pilot to long-term investment requires organisations to show more than adoption rates or productivity gains: they need clear, measurable outcomes the business can understand.

This keynote explores how organisations can define AI success, measure the real impact of their investments, and build a stronger business case for taking AI initiatives beyond the pilot stage.

• Why many AI pilots struggle to demonstrate clear and measurable business value
• How to define credible AI ROI using cost savings, productivity, revenue, risk reduction, and other business outcomes
• How to turn early results into a business case that secures investment and supports AI at scale

As AI systems move into production and become part of critical business processes, organisations need to know when performance starts to change or something goes wrong. Without the right visibility, issues with models, data, infrastructure, or AI agents can go unnoticed until they affect customers or the business.

This interactive think tank will use live audience voting to explore how organisations are monitoring AI in production, where the biggest gaps remain, and what teams are prioritising next.

1. Do you have the monitoring in place to detect model drift before it causes a business impact?
2. Where is your biggest visibility gap: infrastructure, data pipelines, AI agent behaviour, or measuring ROI?
3. Who is responsible when an AI system fails or behaves unexpectedly in production?
4. Are your AI guardrails built into your technology, or do they mainly rely on policies and processes?
5. Where will your next AI investment go: infrastructure, observability, governance, or scaling AI workflows?

Unwind with your peers for a couple of drinks on us!

Beat the rush and join us early for complimentary barista-made coffee and breakfast.

Most enterprise AI is now live, but production is exposing the infrastructure and architecture gaps prototypes never had to face.

This keynote frames what it takes to run AI at enterprise scale, reliably, in systems that can't afford downtime.

• Why scaling AI is now an infrastructure problem, not a model problem
• What breaks first when AI moves into critical, always-on systems
• The architecture decisions that determine whether AI scales or stalls

As Australia's AI governance expectations tighten without a single AI Act, responsible AI has become a moving target for boards and technical teams alike.

This session covers what's actually required now and how to build governance that keeps pace with the rules.

• What regulators expect from AI governance today, in practice
• Building a responsible AI framework that adapts as rules change
• Where governance ownership should sit between technical and executive teams

As AI agents start talking to other AI agents without a human in the loop, traditional access controls don't hold up.

This panel covers how leading teams are applying zero-trust principles to agent-to-agent communication.

• Why agent-to-agent interactions need their own trust model
• Authentication and authorisation approaches built for autonomous agents
• Where zero-trust architecture for agentic AI is still immature

Moving an AI model from a successful pilot to full production can uncover challenges that simply don't appear during testing. As usage grows, teams need to manage performance, reliability, infrastructure, and deployment without disrupting the wider business.

This case study explores how one team scaled an AI model into production, the problems they encountered along the way, and the lessons they learned from the experience.

• The performance and scaling bottlenecks that only emerged at production volume
• How the team approached model versioning, rollback, and safer deployments
• What they learned from the process and what they would do differently next time

A hands-on, interactive session working through a real production AI scenario as a room. Details announced soon.

AI models are only as effective as the data that supports them. Slow, unreliable, or poorly designed data pipelines can impact model performance and make it difficult to scale AI successfully.

This case study explores how one team redesigned its data pipeline to improve speed, reliability, and model performance, while keeping existing systems running throughout the transition.

• How they identified data pipeline issues that were affecting model quality and performance
• How they redesigned the pipeline to reduce delays and improve data reliability
• How they managed the transition without disrupting existing AI workloads

Moving agentic AI from a small pilot into core business functions brings new challenges around scale, security, and governance. These challenges become even more important when AI agents begin working with sensitive financial, employee, and business data.

This case study explores how one organisation expanded agentic AI workflows across finance, HR, and other business functions, while putting the right controls in place and demonstrating measurable value.

• How they designed agentic workflows that could scale beyond the initial use case
• What guardrails and controls they introduced when agents began accessing sensitive business data
• The measurable impact on productivity, efficiency, and business outcomes after rollout

For organisations in regulated industries, adopting AI requires more than policies and good intentions. Teams need practical technical controls that allow AI to operate safely, reliably, and within regulatory requirements.

This panel explores how organisations are building guardrails into AI systems, testing them before deployment, and balancing innovation with the need for security, compliance, and oversight.

• How to design practical AI guardrails that meet regulatory and compliance requirements
• How teams are testing and validating AI before it is used in sensitive or regulated processes
• Where technical controls are essential and policy alone is no longer enough

Small-group, discussion-based sessions where you'll work through real production AI challenges with peers in similar roles. Roundtable topics will be announced soon.

Put your knowledge to the test in this fast-paced quiz covering real-world trivia, key concepts, and emerging trends. Compete for bragging rights — and a voucher — as the top scorer takes the crown.

AI models can change in performance over time as customer behaviour, data, and business conditions evolve. Without the right monitoring in place, these changes can go unnoticed until they begin affecting customers, operations, or revenue.

This case study explores how one team discovered a model drift issue that nearly became a significant business problem, and how they built better observability into their AI pipeline to identify and respond to issues earlier.

• How a model drift incident exposed gaps in their monitoring and almost resulted in a measurable business impact
• How they introduced the right metrics, alerts, and performance thresholds to identify model drift before it became a bigger problem
• How improved AI observability helped the team identify and respond to issues in days rather than weeks

AI adoption is growing quickly, but proving its business value remains a challenge. Moving from a successful pilot to long-term investment requires organisations to show more than adoption rates or productivity gains: they need clear, measurable outcomes the business can understand.

This keynote explores how organisations can define AI success, measure the real impact of their investments, and build a stronger business case for taking AI initiatives beyond the pilot stage.

• Why many AI pilots struggle to demonstrate clear and measurable business value
• How to define credible AI ROI using cost savings, productivity, revenue, risk reduction, and other business outcomes
• How to turn early results into a business case that secures investment and supports AI at scale

As AI systems move into production and become part of critical business processes, organisations need to know when performance starts to change or something goes wrong. Without the right visibility, issues with models, data, infrastructure, or AI agents can go unnoticed until they affect customers or the business.

This interactive think tank will use live audience voting to explore how organisations are monitoring AI in production, where the biggest gaps remain, and what teams are prioritising next.

1. Do you have the monitoring in place to detect model drift before it causes a business impact?
2. Where is your biggest visibility gap: infrastructure, data pipelines, AI agent behaviour, or measuring ROI?
3. Who is responsible when an AI system fails or behaves unexpectedly in production?
4. Are your AI guardrails built into your technology, or do they mainly rely on policies and processes?
5. Where will your next AI investment go: infrastructure, observability, governance, or scaling AI workflows?

Unwind with your peers for a couple of drinks on us!