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Model Deployment & MLOps

Serving models, monitoring, and versioning.

Model Deployment & MLOps

Practical AI requires robust deployment patterns: model serving, APIs, monitoring, and data pipelines for continuous training and evaluation.

Practices

  • Containerize models, use model servers (TorchServe, TensorFlow Serving)
  • Set up monitoring for drift and performance