Developer experience · Empirical study of ML-DevOps adoption trends

An empirical study on ML DevOps adoption trends, efforts, and benefits analysis

Information and Software Technology · 2022

Authors not stated on fetched abstract page

Original paper ↗

Summary

Analyzes ML-DevOps adoption trends, implementation effort and benefits.

For your marketing

Possible founder application

Budget for operational integration, monitoring and skills—not only model development.

Reported result

What the study found

Analyzes ML-DevOps adoption trends, implementation effort and benefits.

Research question

What the authors investigated

How does the study’s stated design explain its reported outcome in this setting?

Design

Empirical study of ML-DevOps adoption trends

Sample and setting

Organizations/practitioners adopting ML DevOps; sample details not shown on fetched abstract page.

Scope and transfer

Limitations to keep in view

Abstract page does not expose full sample or detailed results.

Research sources

abstract_only

Study strength

3.5/5 Abstract-level evidence is useful for framing questions; verify full methods before transfer.

How this was assessed

The 1–5 evidence-check score is a practical reading aid, not a formal peer review or a probability that a conclusion is true. It combines design strength (2 points), sample reporting (1), comparison/control clarity (1), and how much of the paper could be checked (1). Access affects how confidently this directory can assess a study, not the study's underlying quality. Blinding is reported separately because it is not relevant to every design.

Design transparency · 2/2

Based on the reported study design in the retrieved source.

Sample and setting · 1/1

The sample context is stated; a numerical count is preserved only when reported.

Outcome clarity · 1/1

The finding is summarized from publisher or article metadata.

Access for checking · 0.5/1

Only abstract or bibliographic access was confirmed.

Double-blind? The retrieved publisher record does not establish whether participants or outcome assessors were blinded.

Read the paper