Comparison of SageMaker Autopilot, H2O.ai, and TPOT on Amazon Employee Access Dataset Performance

Abstract

Abstract: We present Amazon SageMaker Autopilot: a fully managed system that provides an automatic machine learning solution. Given a tabular dataset and the target column name, Autopilot identifies the problem type, analyzes the data and produces a diverse set of complete ML pipelines, which are tuned to generate a leaderboard of candidate models that the customer can choose from. The diversity allows users to balance between different needs such as model accuracy vs. latency. By exposing not only the final models but the way they are trained, meaning the pipelines, we allow to customize the generated

Research Question

How does the performance of SageMaker Autopilot compare to H2O.ai and TPOT in terms of accuracy and feature selection consistency when evaluated on the Amazon Employee Access dataset with different class imbalances?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Truth-Engine Gate Verdict

This record has not completed Gate 2 of the verification pipeline (a type-checked Lean4 proof for mathematical claims, or a sealed-sandbox reproduction for empirical claims). It is a literature synthesis only. VERIFIED requires an attached reproducible artifact (Lean4 proof source, or repro script and results) before this status can be set; it is not derived from review score or claim count.

Quality Tier

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance