Comparison
amazon-sagemaker-examples vs awesome-mlops
Verdict
Pick amazon-sagemaker-examples if jupyter notebooks for illustrating machine learning workflows on Amazon SageMaker; pick awesome-mlops if awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling.
Markdown twin · amazon-sagemaker-examples alternatives · awesome-mlops alternatives
GraphCanon updated Sep 20, 2026
11views this month
vs
Trust & integrity
| Signal | amazon-sagemaker-examples | awesome-mlops |
|---|---|---|
| Maintenance | Active (10d since push) As of Sep 20, 2026 · github_public_v1 | Dormant (651d since push) As of Sep 4, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 20, 2026 · github_public_v1 | Not a fork · Personal account As of Sep 4, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 15, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · 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
- amazon-sagemaker-examples
- Jupyter notebooks for building, training, and deploying ML models using Amazon SageMaker
- awesome-mlops
- A curated list of references for MLOps
Stars
- amazon-sagemaker-examples
- 11k
- awesome-mlops
- 14k
Forks
- amazon-sagemaker-examples
- 7.0k
- awesome-mlops
- 2.1k
Open issues
- amazon-sagemaker-examples
- 854
- awesome-mlops
- 43
Language
- amazon-sagemaker-examples
- Jupyter Notebook
- awesome-mlops
- -
Adopt for
- amazon-sagemaker-examples
- Jupyter notebooks for illustrating machine learning workflows on Amazon SageMaker
- awesome-mlops
- awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling.
Persona
- amazon-sagemaker-examples
- -
- awesome-mlops
- -
Runtime
- amazon-sagemaker-examples
- -
- awesome-mlops
- -
License
- amazon-sagemaker-examples
- Apache-2.0, allowing free use for any purpose with conditions on attribution and license preservation
- awesome-mlops
- -
Last pushed
- amazon-sagemaker-examples
- Sep 9, 2026
- awesome-mlops
- Nov 21, 2024
Categories
- amazon-sagemaker-examples
- Inference & Serving, Model Training
- awesome-mlops
- Inference & Serving, Model Training
Trust and health
Maintenance
- amazon-sagemaker-examples
- Active (82%)
- awesome-mlops
- Dormant (18%)
Days since push
- amazon-sagemaker-examples
- 10d
- awesome-mlops
- 651d
Open issues (now)
- amazon-sagemaker-examples
- 854
- awesome-mlops
- 43
Stars delta
- amazon-sagemaker-examples
- +6 (30d)
- awesome-mlops
- +56 (30d)
Open issues delta
- amazon-sagemaker-examples
- +5 (30d)
- awesome-mlops
- -1 (30d)
Owner type
- amazon-sagemaker-examples
- Organization
- awesome-mlops
- User
Full report
- amazon-sagemaker-examples
- Trust report
- awesome-mlops
- Trust report
Choose amazon-sagemaker-examples if…
- Tags unique to amazon-sagemaker-examples: aws, deep-learning, inference, jupyter-notebook.
- When you need examples specific to building models with Amazon SageMaker
- More recently updated (last pushed Sep 9, 2026).
When NOT to use amazon-sagemaker-examples
- For non-AWS environments where cost and integration complexities could outweigh benefits
- If seeking open-source tools without ties to a single cloud provider
Choose awesome-mlops if…
- Tags unique to awesome-mlops: ai, devops, engineering, federated-learning.
- If you need references covering online training and inference service architecture patterns, consider awesome-mlops.
- More GitHub stars (14k vs 11k) - visibility, not fit.
When NOT to use awesome-mlops
- Avoid if focused solely on a single MLOps tool or framework as this is a broad resource list.
- Not suitable for those seeking end-to-end support beyond references, like hands-on deployment assistance.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (aws/amazon-sagemaker-examples) · observed Sep 20, 2026
- GitHub forks (aws/amazon-sagemaker-examples) · observed Sep 20, 2026
- Last push (aws/amazon-sagemaker-examples) · observed Sep 9, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
- GitHub stars (visenger/awesome-mlops) · observed Sep 20, 2026
- GitHub forks (visenger/awesome-mlops) · observed Sep 20, 2026
- Last push (visenger/awesome-mlops) · observed Nov 21, 2024
- License file (unknown) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: amazon-sagemaker-examples 11k · awesome-mlops 14k (synced Sep 20, 2026).
Common questions
- What is the difference between amazon-sagemaker-examples and awesome-mlops?
- amazon-sagemaker-examples: Jupyter notebooks for building, training, and deploying ML models using Amazon SageMaker. awesome-mlops: A curated list of references for MLOps. See the comparison table for live GitHub stats and shared categories.
- When should I choose amazon-sagemaker-examples over awesome-mlops?
- Choose amazon-sagemaker-examples over awesome-mlops when Tags unique to amazon-sagemaker-examples: aws, deep-learning, inference, jupyter-notebook; When you need examples specific to building models with Amazon SageMaker; More recently updated (last pushed Sep 9, 2026).
- When should I choose awesome-mlops over amazon-sagemaker-examples?
- Choose awesome-mlops over amazon-sagemaker-examples when Tags unique to awesome-mlops: ai, devops, engineering, federated-learning; If you need references covering online training and inference service architecture patterns, consider awesome-mlops; More GitHub stars (14k vs 11k) - visibility, not fit.
- When should I avoid amazon-sagemaker-examples?
- For non-AWS environments where cost and integration complexities could outweigh benefits If seeking open-source tools without ties to a single cloud provider
- When should I avoid awesome-mlops?
- Avoid if focused solely on a single MLOps tool or framework as this is a broad resource list. Not suitable for those seeking end-to-end support beyond references, like hands-on deployment assistance.
- Is amazon-sagemaker-examples or awesome-mlops more popular on GitHub?
- awesome-mlops has more GitHub stars (14,183 vs 10,990). Stars measure visibility, not whether either tool fits your constraints.
- Are amazon-sagemaker-examples and awesome-mlops open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to amazon-sagemaker-examples or awesome-mlops?
- GraphCanon lists graph-backed alternatives at amazon-sagemaker-examples alternatives and awesome-mlops alternatives (amazon-sagemaker-examples markdown twin, awesome-mlops 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, amazon-sagemaker-examples or awesome-mlops?
- amazon-sagemaker-examples: Active. awesome-mlops: Dormant. 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 amazon-sagemaker-examples and awesome-mlops?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: amazon-sagemaker-examples trust report; awesome-mlops trust report.