A Low Code Structured Approach to Deploying Apache Beam ML Workloads on Kubernetes using BeamStack

We have developed BeamStack, a toolkit tailored to enhance the deployment and management processes of ML and GenAI workloads on Kubernetes. It diminishes the intrinsic complexities associated with the managing of Kubernetes clusters and the deployment of Apache Beam workloads. Fundamentally, BeamStack leverages Beam YAML, a structured format that enables the declarative definition of pipelines. This facilitates rapid deployment and scalability across diverse Kubernetes environments, encompassing cloud-based solutions and local clusters such as Minikube.

BeamStack distinguishes itself through its proficiency in orchestrating AI pipelines within Kubernetes environments. It provides streamlined workflows that enhance the efficiency of setup, deployment, and management processes. Moreover, BeamStack seamlessly integrates with monitoring tools such as Prometheus and Grafana. Its overarching objective is to democratize the deployment of AI workloads with Apache Beam on Kubernetes, empowering users with the confidence to deploy seamlessly while optimizing performance. Our platform’s user-friendly design simplifies the process, making the deployment of Apache Beam jobs universally attainable.

In this talk, we’ll delve into the development journey of BeamStack, a toolkit crafted to simplify the deployment and management of Apache Beam ML workloads on Kubernetes. We’ll explore the motivations behind BeamStack’s creation, the challenges it addresses, and the key components that make it a powerful tool for AI workload deployment.