Comparison
awesome-mlops vs ploomber
Verdict
Pick awesome-mlops if awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML; pick ploomber if ploomber is a Python-based tool that specializes in iterative development and deployment of data pipelines, supporting Jupyter notebooks and integrating smoothly with popular IDEs like PyCharm and VSCode.
Markdown twin · awesome-mlops alternatives · ploomber alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | awesome-mlops | ploomber |
|---|---|---|
| Maintenance | Slowing (97d since push) As of 2w · github_public_v1 | Archived (430d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 2w · github_public_v1 | Not a fork · Organization account As of 3w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- awesome-mlops
- A curated list of awesome MLOps tools.
- ploomber
- The fastest way to build data pipelines. Develop iteratively, deploy anywhere.
Stars
- awesome-mlops
- 5.2k
- ploomber
- 3.6k
Forks
- awesome-mlops
- 762
- ploomber
- 243
Open issues
- awesome-mlops
- 71
- ploomber
- 110
Language
- awesome-mlops
- Python
- ploomber
- Python
Adopt for
- awesome-mlops
- Awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML.
- ploomber
- Ploomber is a Python-based tool that specializes in iterative development and deployment of data pipelines, supporting Jupyter notebooks and integrating smoothly with popular IDEs like PyCharm and VSCode.
Persona
- awesome-mlops
- -
- ploomber
- -
Runtime
- awesome-mlops
- -
- ploomber
- -
License
- awesome-mlops
- -
- ploomber
- Ploomber operates under the Apache License 2.0 which allows free use, modification and distribution, provided that any redistributed code includes an acknowledgement of the original license.
Last pushed
- awesome-mlops
- Apr 29, 2026
- ploomber
- May 29, 2025
Categories
- awesome-mlops
- Developer Tools, Evaluation & Observability, Inference & Serving, Model Training
- ploomber
- Developer Tools
Trust and health
Maintenance
- awesome-mlops
- Slowing (36%)
- ploomber
- Archived (8%)
Days since push
- awesome-mlops
- 97d
- ploomber
- 430d
Archived on GitHub
- awesome-mlops
- No
- ploomber
- Yes
Open issues (now)
- awesome-mlops
- 71
- ploomber
- 110
Owner type
- awesome-mlops
- User
- ploomber
- Organization
Full report
- awesome-mlops
- Trust report
- ploomber
- Trust report
Shared compatibility
- Python · awesome-mlops: Python runtime · ploomber: Python runtime
Choose awesome-mlops if…
- Tags unique to awesome-mlops: ai, awesome, machine-learning-engineering, ml.
- Also covers Evaluation & Observability, Inference & Serving, Model Training.
- You need resources across multiple facets of the machine-learning pipeline, from data validation to model serving.
When NOT to use awesome-mlops
- In search of a single comprehensive tool for end-to-end ML project management; Awesome MLOps is a repository of links rather than a standalone platform.
- Looking for proprietary solutions or detailed vendor-specific documentation as it focuses on broad, open-source offerings.
Choose ploomber if…
- Requirements: Works with Python versions 3.7 and higher..
- Tags unique to ploomber: data-engineering, jupyter-notebooks.
- Use Ploomber when you need to iteratively develop and test data pipelines using Python, as it provides native support for such workflows.
When NOT to use ploomber
- Avoid Ploomber if your development process does not involve iterative testing or if direct integration with Jupyter notebooks is unnecessary.
- Do not use Ploomber if you prefer a non-IDE environment and you do not need Python's ecosystem for building data pipelines, as it heavily integrates with IDEs like PyCharm and VSCode.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (kelvins/awesome-mlops) · observed Aug 4, 2026
- GitHub forks (kelvins/awesome-mlops) · observed Aug 4, 2026
- Last push (kelvins/awesome-mlops) · observed Apr 29, 2026
- License file (unknown) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (ploomber/ploomber) · observed Aug 3, 2026
- GitHub forks (ploomber/ploomber) · observed Aug 3, 2026
- Last push (ploomber/ploomber) · observed May 29, 2025
- License file (Apache-2.0) · observed Aug 3, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: awesome-mlops 5.2k · ploomber 3.6k (synced Aug 4, 2026).
Common questions
- What is the difference between awesome-mlops and ploomber?
- awesome-mlops: A curated list of awesome MLOps tools.. ploomber: The fastest way to build data pipelines. Develop iteratively, deploy anywhere.. See the comparison table for live GitHub stats and shared categories.
- When should I choose awesome-mlops over ploomber?
- Choose awesome-mlops over ploomber when Tags unique to awesome-mlops: ai, awesome, machine-learning-engineering, ml; Also covers Evaluation & Observability, Inference & Serving, Model Training; You need resources across multiple facets of the machine-learning pipeline, from data validation to model serving.
- When should I choose ploomber over awesome-mlops?
- Choose ploomber over awesome-mlops when Requirements: Works with Python versions 3.7 and higher.; Tags unique to ploomber: data-engineering, jupyter-notebooks; Use Ploomber when you need to iteratively develop and test data pipelines using Python, as it provides native support for such workflows.
- When should I avoid awesome-mlops?
- In search of a single comprehensive tool for end-to-end ML project management; Awesome MLOps is a repository of links rather than a standalone platform. Looking for proprietary solutions or detailed vendor-specific documentation as it focuses on broad, open-source offerings.
- When should I avoid ploomber?
- Avoid Ploomber if your development process does not involve iterative testing or if direct integration with Jupyter notebooks is unnecessary. Do not use Ploomber if you prefer a non-IDE environment and you do not need Python's ecosystem for building data pipelines, as it heavily integrates with IDEs like PyCharm and VSCode.
- Is awesome-mlops or ploomber more popular on GitHub?
- awesome-mlops has more GitHub stars (5,229 vs 3,622). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-mlops and ploomber open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to awesome-mlops or ploomber?
- GraphCanon lists graph-backed alternatives at awesome-mlops alternatives and ploomber alternatives (awesome-mlops markdown twin, ploomber markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, awesome-mlops or ploomber?
- awesome-mlops: Slowing. ploomber: Archived. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for awesome-mlops and ploomber?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-mlops trust report; ploomber trust report.