Mirantis is attempting to bridge the widening skills gap between business logic and hardware deployment by launching two specialized training tracks. The company is positioning these courses to address the divergent needs of non-technical staff and infrastructure engineers. By targeting both agentic workflows and GPU cluster operations, Mirantis aims to support the full lifecycle of AI implementation within the enterprise.
Dual-Track Training for AI Workflows and Hardware
The company has introduced two distinct four-module courses designed to address different layers of the AI stack. The first, AI-DEV 100, focuses on agentic workflows and is specifically aimed at non-engineering roles. This course requires no coding knowledge and includes a hands-on lab where participants build a working agentic workflow. Mirantis is positioning this as a way for users who can already interact with AI models to move toward more structured, automated agentic processes without writing software.
The second course, AI-INFRA 100, targets technical professionals responsible for the underlying hardware. This track moves from foundational concepts—distinguishing traditional IT from AI-specific requirements—to advanced topics like cluster provisioning and cluster networking. These modules include hands-on labs to prepare engineers for the complexities of deploying and operating GPU infrastructure for intensive AI workloads.
Scaling AI Capabilities via Kubernetes-Native Infrastructure
Mirantis is leveraging its history in cloud-native operations, such as Kubernetes and OpenStack, to expand into the AI sector. The company describes its core offering as Kubernetes-native infrastructure designed to manage the "Metal-to-Model" lifecycle. Through its k0rdent AI platform and various strategic partnerships, Mirantis seeks to help enterprises maximize GPU utilization and improve cloud economics.
By providing these training modules, Mirantis is attempting to ensure that the human element of the AI stack can keep pace with the rapid evolution of the technology. Dom Wilde, senior vice president and general manager of the enterprise AI cloud business, noted that the speed of AI development necessitates increased learning and collaboration across different organizational functions.
Key Takeaways
- The AI-DEV 100 course enables non-engineers to build agentic workflows through four modules and a hands-on lab without writing code.
- The AI-INFRA 100 course provides technical training on GPU cluster provisioning and cluster networking for AI workloads.
- Mirantis is integrating these courses into its existing training portfolio, which includes cloud-native, container, and Kubernetes expertise.
TechInsyte's Take
In our view, Mirantis is making a calculated move to secure its position in the "AI factory" ecosystem by addressing the primary bottleneck to enterprise adoption: specialized talent. By bifurcating the training into non-coder workflows and deep-stack infrastructure, they are acknowledging that AI success requires both business-level orchestration and heavy-duty hardware management. This strategy suggests that Mirantis views the complexity of GPU orchestration and agentic automation as the new frontline for enterprise IT services and cloud-native infrastructure providers.
Questions & Answers
How does Mirantis differentiate between the two new training tracks?
The AI-DEV 100 course is designed for non-engineers to build agentic workflows without coding, while the AI-INFRA 100 course is for technical professionals to learn GPU cluster provisioning and networking.
What specific technical areas does the infrastructure course cover?
The AI-INFRA 100 course covers the differences between traditional IT and AI infrastructure, as well as in-depth topics including cluster provisioning and cluster networking.
What is the intended outcome for non-technical users in the AI-DEV 100 course?
Participants are expected to learn how to effectively use AI agents and complete a hands-on lab to build a working agentic workflow without writing any code.
How does this training align with Mirantis's broader technology focus?
The courses extend Mirantis's existing expertise in Kubernetes, containers, and OpenStack into the specialized domains of AI agent technology and GPU-based AI infrastructure.
Source: Mirantis
