From September 21–24, I attended “Activating Kubernetes and Private AI on VMware Cloud Foundation,” an advanced bootcamp held at Broadcom’s Atlanta office in Georgia. The four-day program combined technical discussions with hands-on labs, exploring Kubernetes architecture and Private AI infrastructure in enterprise environments.
Designed for Broadcom’s strategic partners, the instructor-led training focused on VCF Architects responsible for developing and delivering VMware Cloud Foundation services and solutions to customers. Throughout the program, the emphasis was on connecting architectural decisions with practical implementation.
Kubernetes: Beyond the Initial Deployment
One of the bootcamp’s two main tracks explored VMware vSphere Kubernetes Service (VKS) architecture. Sessions covered the platform’s full lifecycle, including design, networking, security, migration, and lifecycle management.
GitOps-driven continuous delivery was another important topic, highlighting approaches to managing configuration and changes consistently.
For our work, this broader perspective matters. Building a Kubernetes platform involves planning for day-to-day operations, upgrades, and evolving requirements alongside the initial deployment.
Private AI: From Architecture to Implementation
The second track focused on VMware Private AI Infrastructure, addressing the foundations needed to deploy and operate AI workloads at enterprise scale.
The program progressed from architectural principles to practical deployment, configuration, and optimization. It also explored proven, repeatable implementation patterns that can serve as a foundation for future projects.
This track reinforced the importance of aligning infrastructure decisions with application requirements and business objectives. Platform design, resource allocation, and operational considerations all contribute to how effectively an organization can support AI workloads.
Putting Concepts into Practice
The combination of instructor-led sessions and hands-on labs helped connect architectural concepts with concrete implementation steps. Technical discussions provided context, while the labs offered opportunities to put those concepts into practice.
Together, the Kubernetes and Private AI tracks provided complementary perspectives on developing services on VMware Cloud Foundation, from supporting modern applications to preparing infrastructure for AI workloads.
Bringing the Knowledge Back to Our Team
The bootcamp was an opportunity to deepen my technical knowledge and explore approaches relevant to our customer projects. The topics covered provide useful reference points for assessing requirements, discussing architecture options, and planning implementations.
I return from Atlanta with a broader understanding of VKS and VMware Private AI Infrastructure, along with ideas to share with our team. The next step is to apply that knowledge to the practical challenges our customers face and use it to inform future solution design and delivery.