Collin Newberry

I hear too often the question of "How can I make AI work for my tools?". The short answer is by spending lots of money you don't have. The question we need to ask ourselves instead is "How do I adapt my tools to AI?". We can answer this question by shifting our focus as far left in our support cycle as possible with the architecture of our codified playbooks. Through deliberate coding practices designed to support the ability of AI tools we can seamlessly integrate Large Language Models such as Github Copilot into our workflows. This session will outline a framework for redesigning IaC to work with LLMs and demonstrate the speed of development that can be achieved by doing so.

This session will outline how to use IaC to provide release versioned infrastructure playbooks in a no-code framework. The goal of this is to reduce the cost of cloud infrastructure by maintaining multiple offline copies of the same infrastructure in codified playbooks with zero drift between online and offline environments. We will outline an IaC architectural plan and style guide which will enable the adoption of this strategy and demonstrate the ability to build and deploy numerous environments from offline configuration with zero drift. This framework provides many advantages over traditional IaC including: - Cost reduction in cloud environments - Ability to swing between environments seamlessly with minimal downtime - Seamless DR testing and recovery operations with minimal passive costs - Ability to automate BC tasks for cross regional cloud workloads - Scalability of environments where traditional scaling options do not exist

Traditionally there have been certain aspects of an environment we just can’t replicate. Whether this is due to cost, complexity or the physical-digital crossover it has created a complexity that prevents us from being able to test new deployments in a true replica of production. Digital twins can help us overcome this. The concept of the digital twin was introduced to create a digital representation of a physical object. The goal of this being to allow for simulated inputs and outputs which represent the interactions we have with the physical world. This is applied today in robotics, laboratories and other technology spaces where duplicating devices is cost-prohibitive. We don’t have to limit ourselves to just those high-tech, high-cost devices, we can create digital twins of an entire data center, factory or other environment. This session will introduce you to the benefits of digital twins across industries and how we can take advantage of this emerging technology. Specific c