deployment-paradigms

Batch inference processes large datasets offline at scheduled intervals, while real-time inference serves immediate predictions with low latency. This Skill explains these ML deployment paradigms using Python code examples to demonstrate their distinct latency and cost trade-offs.

To choose the best serving pattern, evaluate your application's latency, cost, and complexity requirements. This Skill compares online, offline, and hybrid serving patterns to help you determine whether a low-latency real-time or cost-effective batch approach suits your ML model.

Yes, this Skill covers edge deployment using tools like TFLite and ONNX. It explains how to deploy machine learning models on edge devices and evaluates the specific trade-offs in latency, cost, and complexity associated with edge ML serving.

Serverless ML deployment is ideal when you need to optimize cost and handle intermittent workloads without managing servers. This Skill explains serverless ML alongside other paradigms, highlighting its specific cost and complexity trade-offs for model serving.

Streaming inference provides continuous, real-time predictions for live data feeds, whereas offline serving handles bulk data asynchronously. This Skill analyzes these ML deployment modes to clarify their differences in latency, cost, and operational complexity.