Seldon

Seldon provides real-time machine learning deployment with enhanced observability for any AI application or system.

What is

Seldon

?

Seldon is a comprehensive MLOps platform that enables organizations to deploy, manage, and monitor machine learning models at scale with enterprise-grade reliability and security. The platform offers multiple deployment solutions including Seldon Core for standardized model deployment, an LLM Module for large language model optimization, and Core+ for enhanced support and accelerator services. With flexible infrastructure options supporting cloud, on-premises, and hybrid deployments, Seldon serves innovative ML teams at companies like Capital One, IKEA, and Volkswagen by simplifying the complexity of production ML systems while maintaining observability and control.

Key Features
  • Seldon Core for standardized model deployment
  • LLM Module for language model optimization
  • Enhanced observability and monitoring
  • Flexible deployment options (cloud, on-prem, hybrid)
  • Cost optimization and resource management
  • Data-centric approach to ML operations
  • Modular architecture for complex systems
  • Enterprise support and accelerator programs
Pricing
  • Contact for pricing
  • Multiple tiers available (Core, Core+, LLM Module)
  • Enterprise solutions with custom pricing
Pros:
  • Over a decade of ML deployment experience
  • Trusted by major enterprises like Capital One and IKEA
  • Standardized workflows reduce complexity
  • Flexible infrastructure deployment options
  • Enhanced observability for production systems
  • Cost optimization capabilities
Cons:
  • Over a decade of ML deployment experience
  • Trusted by major enterprises like Capital One and IKEA
  • Standardized workflows reduce complexity
  • Flexible infrastructure deployment options
  • Enhanced observability for production systems
  • Cost optimization capabilities
Who is it for?
  • ML engineers deploying models at scale
  • Data science teams productionizing models
  • DevOps teams managing AI infrastructure
  • Enterprise AI teams requiring governance
  • Technology companies building AI products
  • Financial services with compliance needs
Best use cases
  • Enterprise ML deployment at scale
  • Model monitoring and drift detection
  • LLM deployment and optimization
  • Multi-cloud ML infrastructure
  • AI system observability
  • Production ML lifecycle management
API Integrations
  • Kubernetes and cloud platform integrations
  • CI/CD pipeline connections
  • Multiple framework support (TensorFlow, PyTorch, etc.)
Security
  • ISO/IEC 27001:2022 certified
  • Enterprise-grade security protocols
  • SOC compliance capabilities
  • Data privacy and protection measures
Implementation
  • Implementation typically takes 2-4 weeks for basic deployment, with 6-8 weeks for full enterprise integration including custom configurations and team training.
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