Features

  • Model Serving: Deploys and manages ML models.
  • Scalability: Scales model deployments on Kubernetes.
  • Integration: Supports various ML frameworks and tools.
  • Monitoring: Provides tools for monitoring model performance.

Benefits

  • Scalability: Easily scales with demand.
  • Efficiency: Simplifies model deployment and management.
  • Flexibility: Supports multiple ML frameworks.
  • Reliability: Ensures high availability and performance.

Use Cases

  • Deploying machine learning models in production.
  • Managing large-scale inference workloads.
  • Monitoring and optimizing model performance.
  • Building scalable AI-driven applications.

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