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Benchmark

As explained in Starting Data Analytics with Generative AI and Python

Benchmarking is the comparison of a system's performance with a reference implementation or established standard metrics.

Benchmarking compares a system’s performance with a reference implementation or established industry metrics. It provides a practical baseline for judging whether an analytics system is efficient. section 7.1

Place benchmarking alongside complementary performance checks:

  • Profiling identifies execution hot spots and memory use. section 7.1
  • Stress testing examines behavior under loads near or beyond expected limits. section 7.1
  • Monitoring tracks performance metrics over time and reveals effects of optimization, changing data volumes, and computational demands. section 7.1

Benchmarking bootstrap analysis

A bootstrap calculation for state-level average sales prices and review scores repeatedly samples data and recalculates statistics. Benchmarking can compare the resulting system performance against a reference implementation or industry-standard metrics, with execution time as the main focus. section 7.1

How Starting Data Analytics with Generative AI and Python covers it

How deep it goes
Defines benchmarking within performance measurement and prioritizes execution time over throughput and resource utilization.
Examples
Uses computationally intensive bootstrap confidence-interval calculations for average sales price and review score by state.
What you can do after
Evaluate analytics-system efficiency by comparing performance against a reference or established metrics.
How it connects
Connects benchmarking with profiling, stress testing, monitoring, execution time, throughput, and resource utilization.

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This explainer was generated by Manning's knowledge graph from the text of Starting Data Analytics with Generative AI and Python. The section links point to the parts of the book it draws on.