Comparison
keras vs awesome-mlops
Verdict
Pick keras when tags unique to keras: neural-networks, deep-learning, python, pytorch; pick awesome-mlops when tags unique to awesome-mlops: engineering, ml, ai, federated-learning.
Markdown twin · keras alternatives · awesome-mlops alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | keras | awesome-mlops |
|---|---|---|
| Maintenance | Very active (4d since push) As of today · github_public_v1 | Dormant (597d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Organization account As of today · github_public_v1 | Not a fork · Personal account As of today · github_public_v1 |
| Security (OSV) | No criticals As of today · osv@v1 | No lockfile As of today · none |
Tagline
- keras
- Deep Learning for humans
- awesome-mlops
- A curated list of references for MLOps
Stars
- keras
- 64k
- awesome-mlops
- 14k
Forks
- keras
- 20k
- awesome-mlops
- 2.1k
Open issues
- keras
- 228
- awesome-mlops
- 42
Language
- keras
- Python
- awesome-mlops
- -
Adopt for
- keras
- -
- awesome-mlops
- -
Persona
- keras
- -
- awesome-mlops
- -
Runtime
- keras
- -
- awesome-mlops
- -
License
- keras
- Apache-2.0
- awesome-mlops
- -
Last pushed
- keras
- Jul 7, 2026
- awesome-mlops
- Nov 21, 2024
Categories
- keras
- Model Training
- awesome-mlops
- Vector Databases, Model Training, Inference & Serving
Trust and health
Maintenance
- keras
- Very active (96%)
- awesome-mlops
- Dormant (18%)
Days since push
- keras
- 4d
- awesome-mlops
- 597d
Open issues (now)
- keras
- 228
- awesome-mlops
- 42
Owner type
- keras
- Organization
- awesome-mlops
- User
Security scan
- keras
- No criticals
- awesome-mlops
- No lockfile
Full report
- keras
- Trust report
- awesome-mlops
- Trust report
Shared compatibility
- Python · keras: Python runtime · awesome-mlops: Python runtime
Choose keras if…
- Tags unique to keras: neural-networks, deep-learning, python, pytorch.
- More GitHub stars (64k vs 14k) - visibility, not fit.
When NOT to use keras
- Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
Choose awesome-mlops if…
- Tags unique to awesome-mlops: engineering, ml, ai, federated-learning.
- Also covers Vector Databases, Inference & Serving.
- Leaner open-issue backlog (42).
When NOT to use awesome-mlops
- Last GitHub push was 597 days ago (dormant maintenance, Nov 21, 2024). Validate activity before betting a new project on awesome-mlops.
- Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.
- Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
- Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (keras-team/keras) · observed Jul 11, 2026
- GitHub forks (keras-team/keras) · observed Jul 11, 2026
- Last push (keras-team/keras) · observed Jul 7, 2026
- License file (Apache-2.0) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (visenger/awesome-mlops) · observed Jul 11, 2026
- GitHub forks (visenger/awesome-mlops) · observed Jul 11, 2026
- Last push (visenger/awesome-mlops) · observed Nov 21, 2024
- License file (unknown) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: keras 64k · awesome-mlops 14k (synced Jul 11, 2026).
Common questions
- What is the difference between keras and awesome-mlops?
- keras: Deep Learning for humans. awesome-mlops: A curated list of references for MLOps. See the comparison table for live GitHub stats and shared categories.
- When should I choose keras over awesome-mlops?
- Choose keras over awesome-mlops when Tags unique to keras: neural-networks, deep-learning, python, pytorch; More GitHub stars (64k vs 14k) - visibility, not fit.
- When should I choose awesome-mlops over keras?
- Choose awesome-mlops over keras when Tags unique to awesome-mlops: engineering, ml, ai, federated-learning; Also covers Vector Databases, Inference & Serving; Leaner open-issue backlog (42).
- When should I avoid keras?
- Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
- When should I avoid awesome-mlops?
- Last GitHub push was 597 days ago (dormant maintenance, Nov 21, 2024). Validate activity before betting a new project on awesome-mlops. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic.
- Is keras or awesome-mlops more popular on GitHub?
- keras has more GitHub stars (64,191 vs 13,952). Stars measure visibility, not whether either tool fits your constraints.
- Are keras and awesome-mlops open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to keras or awesome-mlops?
- GraphCanon lists graph-backed alternatives at keras alternatives and awesome-mlops alternatives (keras 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, keras or awesome-mlops?
- keras: Very 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 keras and awesome-mlops?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: keras trust report; awesome-mlops trust report.