--- title: "comet-examples vs awesome-mlops" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/comet-ml-comet-examples-vs-kelvins-awesome-mlops" tools: ["comet-ml-comet-examples", "kelvins-awesome-mlops"] --- # comet-examples vs awesome-mlops *GraphCanon updated Aug 4, 2026* ## Verdict Pick comet-examples if comet-examples is a collection of machine learning code demonstrations using Comet.ml. It focuses on deep learning algorithms and libraries, providing examples in Jupyter Notebook format; pick awesome-mlops if awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML. [comet-examples](https://github.com/comet-ml/comet-examples) reports 176 GitHub stars, 67 forks, and 26 open issues, last pushed Jul 28, 2026. [awesome-mlops](https://github.com/kelvins/awesome-mlops) has 5.2k stars, 762 forks, and 71 open issues, last pushed Apr 29, 2026. Figures are from public GitHub metadata via [comet-examples's repository](https://github.com/comet-ml/comet-examples) and [awesome-mlops's repository](https://github.com/kelvins/awesome-mlops). | | [comet-examples](/tools/comet-ml-comet-examples.md) | [awesome-mlops](/tools/kelvins-awesome-mlops.md) | | --- | --- | --- | | Tagline | Examples of Machine Learning code using Comet.ml | A curated list of awesome MLOps tools. | | Stars | 176 | 5,229 | | Forks | 67 | 762 | | Open issues | 26 | 71 | | Language | Jupyter Notebook | Python | | Adopt for | Comet-examples is a collection of machine learning code demonstrations using Comet.ml. It focuses on deep learning algorithms and libraries, providing examples in Jupyter Notebook format. | Awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML. | | Persona | - | - | | Runtime | - | - | | License | - | - | | Categories | Evaluation & Observability, Model Training | Developer Tools, Evaluation & Observability, Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [comet-examples](/tools/comet-ml-comet-examples.md) | [awesome-mlops](/tools/kelvins-awesome-mlops.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 5d | 97d | | Open issues (now) | 26 | 71 | | Owner type | Organization | User | | Full report | [trust report](/tools/comet-ml-comet-examples/trust.md) | [trust report](/tools/kelvins-awesome-mlops/trust.md) | ## Shared compatibility - **Python**: [comet-examples](/tools/comet-ml-comet-examples.md) - Python runtime; [awesome-mlops](/tools/kelvins-awesome-mlops.md) - Python runtime ## Decision facts: comet-examples - **Adopt for:** Comet-examples is a collection of machine learning code demonstrations using Comet.ml. It focuses on deep learning algorithms and libraries, providing examples in Jupyter Notebook format. ## Decision facts: awesome-mlops - **Adopt for:** Awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML. ## Choose when ### Choose comet-examples if… - comet-examples is primarily Jupyter Notebook; awesome-mlops is Python. - Tags unique to comet-examples: comet-ml, deep-learning-algorithms, machine-learning-platform, python. - When you are working with deep learning frameworks such as PyTorch or TensorFlow and want to integrate Comet.ml for experiment tracking and model management. ### Choose awesome-mlops if… - awesome-mlops is primarily Python; comet-examples is Jupyter Notebook. - Tags unique to awesome-mlops: ai, awesome, data-science, machine-learning. - Also covers Developer Tools, Inference & Serving. - You need resources across multiple facets of the machine-learning pipeline, from data validation to model serving. ## When NOT to use comet-examples - If you are looking for a tool without third-party dependencies, as Comet-examples necessitates the use of Comet.ml which requires registration. - Avoid using this repository if your project does not need advanced experiment management features and simple code examples would suffice. ## 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. ## Common questions ### What is the difference between comet-examples and awesome-mlops? comet-examples: Examples of Machine Learning code using Comet.ml. awesome-mlops: A curated list of awesome MLOps tools.. See the comparison table for live GitHub stats and shared categories. ### When should I choose comet-examples over awesome-mlops? Choose comet-examples over awesome-mlops when comet-examples is primarily Jupyter Notebook; awesome-mlops is Python; Tags unique to comet-examples: comet-ml, deep-learning-algorithms, machine-learning-platform, python; When you are working with deep learning frameworks such as PyTorch or TensorFlow and want to integrate Comet.ml for experiment tracking and model management. ### When should I choose awesome-mlops over comet-examples? Choose awesome-mlops over comet-examples when awesome-mlops is primarily Python; comet-examples is Jupyter Notebook; Tags unique to awesome-mlops: ai, awesome, data-science, machine-learning; Also covers Developer Tools, Inference & Serving; You need resources across multiple facets of the machine-learning pipeline, from data validation to model serving. ### When should I avoid comet-examples? If you are looking for a tool without third-party dependencies, as Comet-examples necessitates the use of Comet.ml which requires registration. Avoid using this repository if your project does not need advanced experiment management features and simple code examples would suffice. ### 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. ### Is comet-examples or awesome-mlops more popular on GitHub? awesome-mlops has more GitHub stars (5,229 vs 176). Stars measure visibility, not whether either tool fits your constraints. ### Are comet-examples and awesome-mlops open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to comet-examples or awesome-mlops? GraphCanon lists graph-backed alternatives at [comet-examples alternatives](/tools/comet-ml-comet-examples/alternatives) and [awesome-mlops alternatives](/tools/kelvins-awesome-mlops/alternatives) ([comet-examples markdown twin](/tools/comet-ml-comet-examples/alternatives.md), [awesome-mlops markdown twin](/tools/kelvins-awesome-mlops/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/comet-ml-comet-examples-vs-kelvins-awesome-mlops.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, comet-examples or awesome-mlops? comet-examples: Very active. awesome-mlops: Slowing. 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 comet-examples and awesome-mlops? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [comet-examples trust report](/tools/comet-ml-comet-examples/trust); [awesome-mlops trust report](/tools/kelvins-awesome-mlops/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=comet-ml-comet-examples`](/api/graphcanon/graph?tool=comet-ml-comet-examples) - 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/_