Cloud native meets AI: Building Scalable ML Platforms

Cloud Native Night Munich

Join us for an evening at the intersection of cloud-native engineering and AI innovation. Discover how modern teams are architecting scalable ML platforms using Kubernetes and ZenML. We’ll dive into practical strategies for managing machine learning workflows, from experimentation to production, in complex cloud-native environments.
Whether you’re building your first platform or evolving an existing one, you’ll gain valuable insights into patterns, pitfalls, and emerging best practices.
This meetup is ideal for ML engineers, platform architects, DevOps practitioners, and anyone interested in the future of AI infrastructure.

THIS MEETUP WILL BE HYBRID. | Zoom Link will be available shortly before the meetup begins

*** AGENDA ***

• 6.30 PM | Doors open. Grab some snacks & drinks.
• 7.00 PM | Welcome & Intro – Start of hybrid meetup
• 7.15 PM | Abstracting ML Orchestration for Cloud-Native Environments With ZenML, Alexej Penner (MLOps Engineer @ ZenML)
• ~8:15 PM | Architecting and Building a K8s-based AI Platform,
  Mario-Leander Reimer (CTO @ QAware GmbH)
• afterwards: More snacks & drinks, and a lot of time for networking with all attendees and speakers.

*** ABSTRACTS ***

Abstracting ML Orchestration for Cloud-Native Environments With ZenML, Alexej Penner (MLOps Engineer @ ZenML)

In today's rapidly evolving cloud-native landscape, managing ML models at scale presents unique challenges. Options include CNCF projects like Argo and Kubeflow, as well as managed solutions like AWS SageMaker and GCP Vertex AI. Selecting an orchestration framework depends on an organization's legacy stack, expertise, and the integration between data and ML engineering functions.

ZenML, an open-source MLOps framework, bridges the gap between AI and various orchestration backends, offering a standardized architecture for ML workflows. This session explores how ZenML integrates with cloud-native technologies through abstractions that ease traversal across different infrastructure targets. Attendees will learn about common pitfalls in deploying ML models, best practices for reproducible pipelines, and techniques for leveraging appropriate CNCF projects to scale ML workflows.

Architecting and Building a K8s-based AI Platform, Mario-Leander Reimer (CTO @ QAware GmbH)

Developing a scalable and production-ready AI platform poses significant challenges for organisations. Beyond a modular and flexible architecture, critical aspects such as infrastructure automation, orchestration, model deployment, and lifecycle management must be efficiently addressed. Kubernetes and open-source technologies provide a powerful foundation for tackling these challenges.

In this talk, we will explore the conceptual architecture and blueprint of a cloud-native AI platform, outlining the key design principles and best practices that enable scalability, automation, and reproducibility. We will then demonstrate how to build this platform step by step - both locally and in the public cloud - leveraging Kubernetes, open-source tools, and GitOps. The focus will be on creating a highly automated, repeatable, and production-ready environment for machine learning and AI workloads.

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