--- title: "awesome-open-mlops vs keras" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/fuzzylabs-awesome-open-mlops-vs-keras-team-keras" tools: ["fuzzylabs-awesome-open-mlops", "keras-team-keras"] --- # awesome-open-mlops vs keras *GraphCanon updated Jul 12, 2026* ## Verdict Pick awesome-open-mlops when tags unique to awesome-open-mlops: machinelearning, datascience, mlops, infrastructure; pick keras when tags unique to keras: data-science, neural-networks, deep-learning, python. [awesome-open-mlops](https://github.com/fuzzylabs/awesome-open-mlops) reports 482 GitHub stars, 54 forks, and 6 open issues, last pushed May 19, 2025. [keras](http://keras.io/) has 64k stars, 20k forks, and 228 open issues, last pushed Jul 7, 2026. Figures are from public GitHub metadata via [awesome-open-mlops's repository](https://github.com/fuzzylabs/awesome-open-mlops) and [keras's repository](https://github.com/keras-team/keras). | | [awesome-open-mlops](/tools/fuzzylabs-awesome-open-mlops.md) | [keras](/tools/keras-team-keras.md) | | --- | --- | --- | | Tagline | The Fuzzy Labs guide to the universe of open source MLOps | Deep Learning for humans | | Stars | 482 | 64,191 | | Forks | 54 | 19,752 | | Open issues | 6 | 228 | | Language | - | Python | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | AI Agents, Model Training, Inference & Serving | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-open-mlops](/tools/fuzzylabs-awesome-open-mlops.md) | [keras](/tools/keras-team-keras.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 418d | 4d | | Open issues (now) | 6 | 228 | | Security scan | No lockfile | No criticals | | Full report | [trust report](/tools/fuzzylabs-awesome-open-mlops/trust.md) | [trust report](/tools/keras-team-keras/trust.md) | ## Choose when ### Choose awesome-open-mlops if… - Tags unique to awesome-open-mlops: machinelearning, datascience, mlops, infrastructure. - Also covers AI Agents, Inference & Serving. - Leaner open-issue backlog (6). ### Choose keras if… - Tags unique to keras: data-science, neural-networks, deep-learning, python. - More GitHub stars (64k vs 482) - visibility, not fit. ## When NOT to use awesome-open-mlops - Last GitHub push was 419 days ago (dormant maintenance, May 19, 2025). Validate activity before betting a new project on awesome-open-mlops. - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - 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. ## When NOT to use keras - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## Common questions ### What is the difference between awesome-open-mlops and keras? awesome-open-mlops: The Fuzzy Labs guide to the universe of open source MLOps. keras: Deep Learning for humans. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-open-mlops over keras? Choose awesome-open-mlops over keras when Tags unique to awesome-open-mlops: machinelearning, datascience, mlops, infrastructure; Also covers AI Agents, Inference & Serving; Leaner open-issue backlog (6). ### When should I choose keras over awesome-open-mlops? Choose keras over awesome-open-mlops when Tags unique to keras: data-science, neural-networks, deep-learning, python; More GitHub stars (64k vs 482) - visibility, not fit. ### When should I avoid awesome-open-mlops? Last GitHub push was 419 days ago (dormant maintenance, May 19, 2025). Validate activity before betting a new project on awesome-open-mlops. AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. 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. ### When should I avoid keras? Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### Is awesome-open-mlops or keras more popular on GitHub? keras has more GitHub stars (64,191 vs 482). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-open-mlops and keras open source? Yes - both are open-source projects on GitHub (awesome-open-mlops: Apache-2.0, keras: Apache-2.0). ### Where can I find alternatives to awesome-open-mlops or keras? GraphCanon lists graph-backed alternatives at [awesome-open-mlops alternatives](/tools/fuzzylabs-awesome-open-mlops/alternatives) and [keras alternatives](/tools/keras-team-keras/alternatives) ([awesome-open-mlops markdown twin](/tools/fuzzylabs-awesome-open-mlops/alternatives.md), [keras markdown twin](/tools/keras-team-keras/alternatives.md)), 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](/compare/fuzzylabs-awesome-open-mlops-vs-keras-team-keras.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, awesome-open-mlops or keras? awesome-open-mlops: Dormant. keras: Very active. 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-open-mlops and keras? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-open-mlops trust report](/tools/fuzzylabs-awesome-open-mlops/trust); [keras trust report](/tools/keras-team-keras/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=fuzzylabs-awesome-open-mlops`](/api/graphcanon/graph?tool=fuzzylabs-awesome-open-mlops) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_