The machine learning operations
and management platform
(1:33)
Algorithmia is MLOps
Algorithmia automates ML deployment, provides maximum tooling flexibility, optimizes collaboration between operations and development, leverages existing SDLC and CI/CD practices, and includes advanced security and governance features—in the cloud, on-premises, or as a fully managed service.
services
operations
Deployment
Connect data sources and load pipelines from any tool, language, or platform. Catalog versions and validate models to ensure all dependencies are operational.
Deployment
Connect data sources and load pipelines from any tool, language, or platform. Catalog versions and validate models to ensure all dependencies are operational.
- Training integration Allow your data scientists to work with the tools that work for them
- Algorithm pipelining Serve and scale high performance and complex ML applications
- Data services Connect your data sources to one central location
- Model management Control access to your ML lifecycle to align your business goals
- Model registration Build, discover, re-use, and manage your ML assets
Operations
Continuously monitor model performance and resource consumption—balancing operational costs and efficiency.
Operations
Continuously monitor model performance and resource consumption—balancing operational costs and efficiency.
- Model operations Control model usage and performance in production
- Monitoring and reporting Visibility into model consumption, call details, and server use
- Infrastructure management Deploy mission-critical ML applications where and when you need them
Governance and security
Protect models from tampering with an enterprise-class framework of access controls, security APIs, and system encryption.
Governance and security
Protect models from tampering with an enterprise-class framework of access controls, security APIs, and system encryption.
- Governance Manage your ML lifecycle with tools to support internal and external compliance
- Security MLOps security best practices built in as a first-order consideration
