This company develops AI infrastructure software to simplify the adoption of artificial intelligence technologies. Their platform provides researchers and engineers with standardized, scalable access to necessary computing resources from any location. The software automates the entire lifecycle of AI projects, from initial research and development through deployment and servitization.
Funding
$10.2M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
AI developers and researchers often face challenges in efficiently managing and scaling their computing resources, leading to bottlenecks in model development, training, and deployment. Inefficient resource utilization and the complexity of managing diverse computing environments further exacerbate these issues.
Solution
Lablup provides Backend.AI, a streamlined, container-based computing cluster platform designed to optimize AI workflows. The platform offers GPU virtualization and resource management capabilities, enabling users to efficiently build, train, and serve AI models of any size. Backend.AI supports various computing and ML frameworks, diverse programming languages, and pluggable heterogeneous accelerator support, including CUDA GPU, ROCm GPU, TPU, and IPU. It simplifies the deployment of AI models by providing an easy-to-use backend service that integrates with existing development tools and platforms, whether on-cloud, on-premises, or hybrid environments.
Target Audience
The primary target audience includes AI developers, researchers, data scientists, and enterprises that require scalable and efficient computing resources for AI model development, training, and deployment.
Features
- Container-based computing cluster platform for AI workloads
- GPU virtualization and fractional GPU allocation for optimal resource utilization
- Support for various computing and ML frameworks, including TensorFlow, PyTorch, and scikit-learn
- Pluggable heterogeneous accelerator support (CUDA, ROCm, TPU, IPU)
- Web UI for managing resources, monitoring performance, and visualizing data
- RESTful APIs and client libraries for seamless integration with existing tools and workflows
- Scalable architecture for handling large datasets and complex models
- Role-based access control and security features for protecting sensitive data