Model serving infrastructure

Scales to handle production workloads with request-based autoscaling
Ensures high availability with availability zones and automated instance restarts
Runs inference on spot instances with on-demand backups
Manages traffic splitting for A/B testing
Supports realtime and batch workloads

Reproducible model deployments

Deploy TensorFlow, PyTorch, sklearn and other models
Implement request handling in Python
Customize compute, autoscaling, and networking for each API
Package dependencies, code, and configuration for reproducible deployments
Test locally before deploying to your cluster

Machine learning API management

Monitor API performance
Aggregate and stream logs
Customize prediction tracking
Update APIs without downtime
Integrate with your existing stack

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