Frequently Asked Questions
What is the focus of the track?
The focus of the DevOps, CI/CD & Platform Engineering track is the operational foundation that makes AI systems reliable in production. It moves the conversation beyond the AI model itself, focusing instead on the infrastructure discipline, delivery pipelines, and observability required to manage complex, AI-augmented systems. You will learn how to treat inference costs, resource utilization, and production readiness as core engineering decisions rather than administrative or procurement tasks.
What topics are covered under CI/CD Pipelines in this track?
The curriculum dives into automation, advanced testing practices (like Test‑Driven Development, TDD), and effective deployment strategies to accelerate software delivery.
Why does this track prioritize the platform over the AI model for reliability?
The track is built on the principle that while a model provides a capability, the platform is what makes it trustworthy. You will learn how to design the conditions—such as robust delivery pipelines and infrastructure discipline—under which AI systems can operate reliably without failing when faced with real-world production demands.
How will we learn to manage the unique costs associated with AI workloads?
Instead of leaving costs to procurement, this track teaches you to treat inference costs and resource utilization as operational engineering decisions. You will learn strategies for managing AI infrastructure economics directly within the platform to ensure your systems remain sustainable and efficient.
What does observability look like for a non-deterministic AI system?
Standard monitoring isn't enough for AI. This track covers how to implement metrics, logs, and tracing specifically for distributed and AI-augmented systems. You will learn how to achieve transparency in your stack, allowing you to monitor unpredictable workloads and latency characteristics effectively.
How can Platform Engineering improve the delivery of AI-enabled software?
The track focuses on building Internal Developer Platforms (IDPs) and leveraging Kubernetes to create standardized capabilities. You will learn how to build the infrastructure that allows teams to deliver and operate software effectively, abstracting the complexity of the underlying cloud-native environment.
How do we integrate security into AI operations without slowing down the pipeline?
The track emphasizes Security & Production Readiness as a core component of the platform. You will learn how to bake security and compliance into your operational systems and automated pipelines before they reach production, ensuring that "fast delivery" doesn't compromise the safety of the system.
