deploy-ml-model-serving

Deploy machine learning models to production with scalable serving infrastructure, monitoring, and A/B testing.

Use MLflow's built-in serving for quick deployment of scikit-learn, PyTorch, and TensorFlow models.

Expected: Model server starts successfully, responds to HTTP POST requests, returns predictions in JSON format, Docker container runs without errors.

Deploy machine learning models to production serving infrastructure using MLflow, BentoML, or Seldon Core with REST/gRPC endpoints, implement autoscaling, monitoring, and A/B testing capabilities for high-performance model inference at scale. Use when deploying trained models for real-time inference, setting up REST or gRPC prediction APIs, implementing autoscaling for variable load, running A/B tests between model versions, or migrating from batch to real-time inference. Source: pjt222/development-guides.