Deployment of ML Models

Building a model is only the first step toward real-world impact. It teaches model serving, API integration, and cloud deployment strategies. Learners explore containerization and scalable inference systems. Monitoring performance and updating models in production is emphasized. Deployment skills ensure AI solutions reach end users effectively. This bridges research and industry practice.

Deployment Elements:

  • Production integration
  • Scalability and monitoring
  • Cloud-based deployment

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