model-serving

You can deploy ML models for production inference by installing BentoML or Triton, preparing your trained model, and running the provided deployment workflow scripts. This enables automated deployment to cloud, on-premises, or edge targets with optimized performance.

Configuring autoscaling for deployed ML models is handled through the Skill's deployment scripts, which set up auto-scaling rules and basic monitoring integration to maintain latency budgets and ensure resource utilization adapts to inference demand.

Yes, you can optimize inference latency using quantization and hardware acceleration. The Skill provides inference optimization capabilities specifically designed to reduce latency and improve resource utilization during production model serving.

Yes, model deployment works for both cloud and edge targets. The Skill supports automated deployment of ML models across cloud, on-premises, and edge environments, ensuring inference capabilities are available across diverse infrastructure setups.

Yes, you need yaml to run the model serving deployment workflow. Yaml is listed as a required dependency to configure and execute the scripts that deploy ML models for production inference.

Using BentoML vs Triton for ML model serving depends on your deployment target. Both are supported by the Skill to handle automated deployment, inference optimization, and scaling, allowing you to choose the framework that best fits your infrastructure.