About DomaLearn

Empowering AI Research with Reliable Infrastructure

DomaLearn specializes in delivering end-to-end infrastructure solutions for AI research and development. Our platform integrates compute orchestration, scalable storage systems, and monitoring tools to support teams running experiments at any scale. Built with modularity in mind, our services allow organizations to customize workflows for data ingestion, preprocessing, model training, and evaluation without the complexity of maintaining disparate systems.

Our Mission

Our mission is to streamline the deployment and management of AI learning environments, enabling researchers and developers to focus on innovation rather than infrastructure overhead. Based in Zurich, Switzerland, we collaborate with leading institutions to deliver solutions that adapt to evolving research needs in 2025 and beyond.

Modular Architecture

Leverage a microservices-based design that lets you plug in new components or replace existing ones without disrupting ongoing experiments.

Flexible Deployment

Choose between on-premises clusters at ETH Zurich or cloud-based resources, tailoring resource allocation to your project requirements.

Seamless Integration

Integrate with popular machine learning frameworks and data processing tools via standardized APIs and SDKs for a unified workflow.

Automated Monitoring

Secure Data Management

User-Friendly Dashboard

Scalable Orchestration

Key Capabilities

DomaLearn provides a suite of features designed to simplify every stage of the AI lifecycle. From data ingestion pipelines to real-time resource monitoring, our platform adapts to diverse research workloads and accelerates time to insight.

Content 1

Data Pipeline Automation

Automate data collection and preprocessing tasks, ensuring consistency and repeatability across experiments.

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Content 2

Compute Resource Scheduling

Efficiently allocate GPU and CPU resources using our scheduler to optimize utilization and reduce idle time.

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Content 3

Experiment Tracking

Track model versions, hyperparameters, and metrics in an interactive interface for transparent reproducibility.

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