Bedrock + ML cost
Identify ML/Bedrock cost patterns.
5 lessons. 1 quiz.
Read in order, or jump to what you need.
The ML cost landscape
By the end of this lesson, you will be able to map ML/Bedrock cost surface to specific optimization levers, identify the major cost drivers, and recognize which Bedrock rule applies to each pattern.
The Bedrock rule family, RC-1601 to RC-1634
By the end of this lesson, you will be able to name what each Bedrock rule actually detects, predict which ones can produce a dollar figure, and route each finding to the right owner.
Model selection trade-offs
By the end of this lesson, you will be able to pick the right model for a workload, trade off cost vs capability, and execute a mixed-routing pattern.
Batch processing for cost reduction
By the end of this lesson, you will be able to convert real-time inference to batch when latency permits, calculate the ~50% savings, and combine batch with model selection for compounding savings.
Provisioned throughput optimization
By the end of this lesson, you will be able to decide between on-demand and provisioned throughput, right-size provisioned capacity, and schedule provisioned throughput for time-aware optimization.
Module quiz.
Take it once the lessons are done.