Union Cloud
What is Union Cloud?
Union AI is an infrastructure AI tool designed for managing ML and data workloads.It offers a scalable MLOps platform that provides full data lineage, versioning, caching, observability, and reproducibility.
By leveraging a Kubernetes-powered infrastructure, Union optimizes resources and reduces costs by up to 66%.This tool supports multi-cloud environments and fits seamlessly within the cloud ecosystem, ensuring tailored infrastructure that can adapt to various technical demands.
With features like task-level resource monitoring, live-logging, and built-in dashboards, Union simplifies the debugging process and accelerates infrastructure optimization for faster experimentation and deployment of ML models.
This AI orchestrator is a better replacement for airflow and kubeflow, offering a purpose-built lineage-aware pipeline orchestration solution with features like experiment tracking, cross-team task sharing, and compile-time error checking.
Union.ai promotes reproducibility, auditability, and efficient workflow management for streamlined AI infrastructure development and deployment.
KEY FEATURES
- ✔️ Scalable MLOps platform.
- ✔️ Supports multi-cloud environments.
- ✔️ Task-level resource monitoring.
- ✔️ Live-logging.
- ✔️ Built-in dashboards.
USE CASES
- Easily manage and optimize ML workloads across multi-cloud environments using Union.ai's scalable MLOps platform with full data lineage and versioning capabilities, leading to improved resource utilization and cost savings of up to 66%..
- Accelerate infrastructure optimization and debugging processes by leveraging Union.ai's task-level resource monitoring, live-logging, and built-in dashboards, enabling faster experimentation and deployment of ML models with streamlined workflow management..
- Ensure reproducibility, auditability, and efficient workflow management in AI infrastructure development by utilizing Union.ai as a purpose-built lineage-aware pipeline orchestration tool, with features like experiment tracking, cross-team task sharing, and compile-time error checking for seamless deployment of ML models..
