How deeply does Effective Platform Engineering cover experimentation?

Requires proof-of-concept implementation and alpha-user testing, emphasizing incremental evidence rather than abstract technology selection.

Experimentation

As explained in Effective Platform Engineering

Experimentation is the systematic evaluation of alternative designs or implementations under realistic conditions to inform decisions.

Experimentation systematically evaluates alternative platform designs or technologies under realistic conditions so measured results guide decisions. It replaces abstract preference with evidence from the actual engineering platform. section 8.2.1 section 2.4

Apply experimentation before committing to platform capabilities:

Build a proof of concept on the actual engineering platform infrastructure, especially when installation, upgrades, or maintenance may vary in a Kubernetes context. section 8.2.1
Measure expected value against software selection criteria instead of accepting a vendor or technology choice without implementation evidence. section 8.2.1 section 2.4
Test the experience with alpha users early, introducing fundamental capabilities before complex implementations and using feedback to shape the next step. section 2.4

Choosing a platform service

Before adopting a service or extension, the platform team installs it in the real platform environment, evaluates operational effort and expected value, and observes how early users experience the capability. The team can then refine the design or reject the choice before it creates handoffs, ticket queues, or friction. section 8.2.1 section 2.4

How Effective Platform Engineering covers it

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Also explained in

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Machine Learning Platform EngineeringA/B testing is a controlled comparison of two or more variants by assigning them to different users or inputs and measuring their outcomes.
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Experimentation for EngineersA controlled investigation that measures the effect of changing one or more system factors while accounting for other influences.
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Effective Data AnalysisAn experiment is a procedure that evaluates the effect of an intervention by comparing outcomes under different conditions.
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First-Party Data ActivationExperimentation is the systematic use of experiments to compare marketing interventions and learn which produce better outcomes.
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Machine Learning System DesignAn A/B test is a controlled experiment that compares outcomes for users or units exposed to two alternative variants.
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The Art of AI Product DevelopmentExperimentation is the deliberate variation and evaluation of product or system conditions to learn what works.
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Platform Engineering on KubernetesA/B testing is a technique that compares alternative versions of an application or feature by exposing different user groups to each version and measuring their outcomes.
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Tech Startup ToolkitA controlled experiment is a structured test that changes selected conditions while observing outcomes to evaluate a hypothesis.
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AI-Powered SearchA/B testing is an experiment that compares two or more variants by randomly assigning users and measuring outcome differences.
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Architecture Modernization: Socio-technical alignment of software, strategy, and structureA/B testing is an experiment that compares two variants of a product or experience with different users to evaluate their relative outcomes.
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Think Like a Software Engineering ManagerExperimentation is the deliberate testing of ideas or changes to learn from observed results.
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WordPress Power Toolkit: Harness AI to build next-level websitesA/B testing is an experimental technique that compares two or more variants to determine which performs better against a chosen outcome.
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Delivering Machine Learning Projects: From design to deploymentAn experiment is a structured investigation in which an uncertain proposition is tested through planned observations or actions.
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Introduction to Generative AI, Second EditionA/B testing is an experimental method that compares alternative versions by measuring their performance under controlled conditions.
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Causal AIAn A/B test is an experiment that compares outcomes under two assigned variants, commonly labeled A and B.

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Related concepts

control groupuses · A control group is a set of experimental units assigned to a baseline or comparison condition against which an intervention is evaluated.
study designincludes · Study design is the planned arrangement of procedures, conditions, measurements, and comparisons used to investigate a question and support valid conclusions.
observational datacontrasts with · Observational data are measurements collected without the investigator assigning or controlling the conditions, treatments, or interventions being studied.
simulationalternative to · A simulation is a modeled or controlled enactment of a system, process, or scenario used to examine or reproduce behavior under specified conditions.
metricmeasures · A metric is a quantitative measure used to characterize or assess a property, behavior, performance, or outcome.

This explainer was generated by Manning's knowledge graph from the text of Effective Platform Engineering. The section links point to the parts of the book it draws on.