VESSL AI offers an end-to-end MLOps platform that enables machine learning teams to build, train, and deploy models efficiently across various infrastructures with a single command. The platform addresses the challenges of resource management and deployment speed by providing serverless deployment, real-time monitoring, and automated CI/CD workflows.
Funding
$16.4M 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
Machine learning teams face challenges in efficiently building, training, and deploying models across diverse infrastructures. Managing resources, ensuring deployment speed, and automating continuous integration and continuous delivery (CI/CD) workflows can be complex and time-consuming.
Solution
VESSL AI offers an end-to-end MLOps platform designed to streamline the entire machine learning lifecycle. The platform enables teams to build, train, and deploy models across various infrastructures using a unified interface and a single command. By providing serverless deployment capabilities, real-time monitoring, and automated CI/CD pipelines, VESSL AI addresses the complexities of resource management and deployment speed. The platform allows users to fine-tune and deploy open-source models with zero setup, optimize costs with GPU usage and spot instances, and scale inference with ease. VESSL AI simplifies complex infrastructure setups, enabling machine learning teams to focus on model development and innovation.
Target Audience
VESSL AI targets machine learning teams and AI researchers who require efficient tools for building, training, and deploying models at scale across various infrastructures.
Features
- Serverless deployment with persistent endpoints for optimized resource utilization.
- Real-time monitoring of system and inference metrics, including worker count, GPU utilization, latency, and throughput.
- Automated CI/CD pipelines for end-to-end workflow automation.
- Unified web console and command-line interface across cloud providers and on-premise clusters.
- Support for custom resource specifications, including GPU type and quantity.
- Batch job scheduling with per-second billing for cost efficiency.
- Integration with open-source models and AI workloads with zero setup.
- Traffic management for A/B testing and model evaluation.