--- title: "best-data-science-resources vs awesome-mlops" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/mohitkr95-best-data-science-resources-vs-visenger-awesome-mlops" tools: ["mohitkr95-best-data-science-resources", "visenger-awesome-mlops"] --- # best-data-science-resources vs awesome-mlops *GraphCanon updated Aug 4, 2026* ## Verdict Pick best-data-science-resources if best-data-science-resources is a curated collection of data science learning materials designed for skills and interview preparation, focusing on industry-driven content; pick awesome-mlops if awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling. [best-data-science-resources](https://github.com/Mohitkr95/best-data-science-resources) reports 528 GitHub stars, 140 forks, and 0 open issues, last pushed Apr 14, 2023. [awesome-mlops](https://ml-ops.org) has 14k stars, 2.1k forks, and 44 open issues, last pushed Nov 21, 2024. Figures are from public GitHub metadata via [best-data-science-resources's repository](https://github.com/Mohitkr95/best-data-science-resources) and [awesome-mlops's repository](https://github.com/visenger/awesome-mlops). | | [best-data-science-resources](/tools/mohitkr95-best-data-science-resources.md) | [awesome-mlops](/tools/visenger-awesome-mlops.md) | | --- | --- | --- | | Tagline | Curated Data Science Resources | A curated list of references for MLOps | | Stars | 528 | 14,127 | | Forks | 140 | 2,101 | | Open issues | 0 | 44 | | Language | Jupyter Notebook | - | | Adopt for | best-data-science-resources is a curated collection of data science learning materials designed for skills and interview preparation, focusing on industry-driven content. | awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling. | | Persona | - | - | | Runtime | - | - | | License | MIT | - | | Categories | Data & Retrieval, Model Training | Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [best-data-science-resources](/tools/mohitkr95-best-data-science-resources.md) | [awesome-mlops](/tools/visenger-awesome-mlops.md) | | --- | --- | --- | | Days since push | 1204d | 621d | | Open issues (now) | 0 | 44 | | Full report | [trust report](/tools/mohitkr95-best-data-science-resources/trust.md) | [trust report](/tools/visenger-awesome-mlops/trust.md) | ## Decision facts: best-data-science-resources - **Hosting:** self hosted - best-data-science-resources is hosted on GitHub as a repository with open-source resources available to anyone. - **Pricing:** freemium - The resources are free of cost and made accessible under MIT License, but advanced training materials or certifications related services may incur costs elsewhere. - **Requirements:** It is recommended to have a basic understanding of programming languages like Python and concepts in data science to derive maximum benefit from the resources. - **Adopt for:** best-data-science-resources is a curated collection of data science learning materials designed for skills and interview preparation, focusing on industry-driven content. ## Decision facts: awesome-mlops - **Adopt for:** awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling. ## Choose when ### Choose best-data-science-resources if… - best-data-science-resources is hosted on GitHub as a repository with open-source resources available to anyone. - Pricing: The resources are free of cost and made accessible under MIT License, but advanced training materials or certifications related services may incur costs elsewhere.. - Requirements: It is recommended to have a basic understanding of programming languages like Python and concepts in data science to derive maximum benefit from the resources.. - Tags unique to best-data-science-resources: artificial-intelligence, computer-vision, deep-learning, natural-language-processing. - Also covers Data & Retrieval. - When you need comprehensive resources covering areas like machine learning, deep learning, natural language processing, and computer vision for both skill development and job readiness. ### Choose awesome-mlops if… - Tags unique to awesome-mlops: data-science, devops, engineering, federated-learning. - Also covers Inference & Serving. - If you need references covering online training and inference service architecture patterns, consider awesome-mlops. ## When NOT to use best-data-science-resources - When you require hands-on project-based training that focuses on applying concepts rather than just theoretical learning and resource lists. - If you're pursuing advanced certification courses, as the repository is more suited for self-study and does not provide formal accredited training materials or certifications. ## 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. ## Common questions ### What is the difference between best-data-science-resources and awesome-mlops? best-data-science-resources: Curated Data Science Resources. awesome-mlops: A curated list of references for MLOps. See the comparison table for live GitHub stats and shared categories. ### When should I choose best-data-science-resources over awesome-mlops? Choose best-data-science-resources over awesome-mlops when best-data-science-resources is hosted on GitHub as a repository with open-source resources available to anyone; Pricing: The resources are free of cost and made accessible under MIT License, but advanced training materials or certifications related services may incur costs elsewhere.; Requirements: It is recommended to have a basic understanding of programming languages like Python and concepts in data science to derive maximum benefit from the resources.; Tags unique to best-data-science-resources: artificial-intelligence, computer-vision, deep-learning, natural-language-processing; Also covers Data & Retrieval; When you need comprehensive resources covering areas like machine learning, deep learning, natural language processing, and computer vision for both skill development and job readiness. ### When should I choose awesome-mlops over best-data-science-resources? Choose awesome-mlops over best-data-science-resources when Tags unique to awesome-mlops: data-science, devops, engineering, federated-learning; Also covers Inference & Serving; If you need references covering online training and inference service architecture patterns, consider awesome-mlops. ### When should I avoid best-data-science-resources? When you require hands-on project-based training that focuses on applying concepts rather than just theoretical learning and resource lists. If you're pursuing advanced certification courses, as the repository is more suited for self-study and does not provide formal accredited training materials or certifications. ### 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 best-data-science-resources or awesome-mlops more popular on GitHub? awesome-mlops has more GitHub stars (14,127 vs 528). Stars measure visibility, not whether either tool fits your constraints. ### Are best-data-science-resources and awesome-mlops open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to best-data-science-resources or awesome-mlops? GraphCanon lists graph-backed alternatives at [best-data-science-resources alternatives](/tools/mohitkr95-best-data-science-resources/alternatives) and [awesome-mlops alternatives](/tools/visenger-awesome-mlops/alternatives) ([best-data-science-resources markdown twin](/tools/mohitkr95-best-data-science-resources/alternatives.md), [awesome-mlops markdown twin](/tools/visenger-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/mohitkr95-best-data-science-resources-vs-visenger-awesome-mlops.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, best-data-science-resources or awesome-mlops? best-data-science-resources: Dormant. 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 best-data-science-resources and awesome-mlops? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [best-data-science-resources trust report](/tools/mohitkr95-best-data-science-resources/trust); [awesome-mlops trust report](/tools/visenger-awesome-mlops/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=mohitkr95-best-data-science-resources`](/api/graphcanon/graph?tool=mohitkr95-best-data-science-resources) - 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/_