Seldon is a machine learning deployment platform that enables organizations to deploy and manage models at scale, reducing deployment time from months to minutes. By providing production-ready inference servers and advanced experimentation tools, Seldon enhances operational efficiency and reduces infrastructure costs, delivering an average productivity gain of 85%.
Funding
$33.7M 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.
CIGBFounders
Product
Problem
Organizations face challenges in deploying and managing machine learning models at scale, often encountering lengthy deployment times and difficulties in optimizing infrastructure resource allocation. This complexity hinders operational efficiency and increases infrastructure costs associated with model deployment.
Solution
Seldon provides a machine learning deployment platform that streamlines the deployment and management of models at scale. The platform offers production-ready inference servers optimized for popular ML frameworks, along with advanced experimentation tools such as multi-armed bandits, A/B tests, shadow deployments, and canary deployments. Seldon enables organizations to optimize infrastructure resource allocation, reducing deployment time from months to minutes and delivering significant productivity gains. The platform also provides features for monitoring model performance, managing model versions, and ensuring governance and compliance.
Target Audience
Seldon targets organizations that need to deploy and manage machine learning models at scale, including data science teams, machine learning engineers, and IT operations professionals.
Features
- Production-ready inference servers optimized for popular ML frameworks
- Advanced experimentation and traffic splitting capabilities, including A/B tests and multi-armed bandits
- Automated infrastructure resource allocation to optimize costs
- Custom alerts for deviations in model metrics, enabling proactive responses to unexpected behavior
- Model versioning and rollback capabilities for mitigating unforeseen risks
- Advanced user management for granular policies and regulatory compliance
- Audit trails, logging, and alerts for governance and troubleshooting
- LLM Module for deploying and managing GenAI models
- Integration with MLServer, a lightweight inference server
- Integration with Alibi for model explainability and monitoring