--- title: "Spearmint vs awesome-mlops" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hips-spearmint-vs-visenger-awesome-mlops" tools: ["hips-spearmint", "visenger-awesome-mlops"] --- # Spearmint vs awesome-mlops *GraphCanon updated Aug 4, 2026* ## Verdict Pick Spearmint if a specialized package for performing Bayesian optimization, Spearmint automates experiment running and parameter tuning to minimize objectives efficiently; pick awesome-mlops if awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling. [Spearmint](https://github.com/HIPS/Spearmint) reports 1.6k GitHub stars, 327 forks, and 77 open issues, last pushed Dec 27, 2019. [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 [Spearmint's repository](https://github.com/HIPS/Spearmint) and [awesome-mlops's repository](https://github.com/visenger/awesome-mlops). | | [Spearmint](/tools/hips-spearmint.md) | [awesome-mlops](/tools/visenger-awesome-mlops.md) | | --- | --- | --- | | Tagline | Bayesian optimization codebase | A curated list of references for MLOps | | Stars | 1,573 | 14,127 | | Forks | 327 | 2,101 | | Open issues | 77 | 44 | | Language | Python | - | | Adopt for | A specialized package for performing Bayesian optimization, Spearmint automates experiment running and parameter tuning to minimize objectives efficiently. | awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling. | | Persona | - | - | | Runtime | - | - | | License | Other | - | | Categories | Model Training | Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Spearmint](/tools/hips-spearmint.md) | [awesome-mlops](/tools/visenger-awesome-mlops.md) | | --- | --- | --- | | Days since push | 2411d | 621d | | Open issues (now) | 77 | 44 | | Owner type | Organization | User | | Full report | [trust report](/tools/hips-spearmint/trust.md) | [trust report](/tools/visenger-awesome-mlops/trust.md) | ## Shared compatibility - **Python**: [Spearmint](/tools/hips-spearmint.md) - Python runtime; [awesome-mlops](/tools/visenger-awesome-mlops.md) - Python runtime ## Decision facts: Spearmint - **Adopt for:** A specialized package for performing Bayesian optimization, Spearmint automates experiment running and parameter tuning to minimize objectives efficiently. ## Decision facts: awesome-mlops - **Adopt for:** awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling. ## Choose when ### Choose Spearmint if… - Tags unique to Spearmint: automated-experimentation, bayesian-optimization, hyperparameter-tuning. - - When you require automated experimentation with parameters that can be iteratively adjusted ### Choose awesome-mlops if… - Tags unique to awesome-mlops: ai, data-science, devops, engineering. - Also covers Inference & Serving. - If you need references covering online training and inference service architecture patterns, consider awesome-mlops. ## When NOT to use Spearmint - - If your project requires a permissive license as Spearmint operates under an Academic and Non-Commercial Research Use License - - If you need real-time or continuous parameter tuning outside of batch experimentation contexts as Spearmint is suited for controlled experiment setups ## 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 Spearmint and awesome-mlops? Spearmint: Bayesian optimization codebase. awesome-mlops: A curated list of references for MLOps. See the comparison table for live GitHub stats and shared categories. ### When should I choose Spearmint over awesome-mlops? Choose Spearmint over awesome-mlops when Tags unique to Spearmint: automated-experimentation, bayesian-optimization, hyperparameter-tuning; - When you require automated experimentation with parameters that can be iteratively adjusted. ### When should I choose awesome-mlops over Spearmint? Choose awesome-mlops over Spearmint when Tags unique to awesome-mlops: ai, data-science, devops, engineering; Also covers Inference & Serving; If you need references covering online training and inference service architecture patterns, consider awesome-mlops. ### When should I avoid Spearmint? - If your project requires a permissive license as Spearmint operates under an Academic and Non-Commercial Research Use License - If you need real-time or continuous parameter tuning outside of batch experimentation contexts as Spearmint is suited for controlled experiment setups ### 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 Spearmint or awesome-mlops more popular on GitHub? awesome-mlops has more GitHub stars (14,127 vs 1,573). Stars measure visibility, not whether either tool fits your constraints. ### Are Spearmint and awesome-mlops open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to Spearmint or awesome-mlops? GraphCanon lists graph-backed alternatives at [Spearmint alternatives](/tools/hips-spearmint/alternatives) and [awesome-mlops alternatives](/tools/visenger-awesome-mlops/alternatives) ([Spearmint markdown twin](/tools/hips-spearmint/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/hips-spearmint-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, Spearmint or awesome-mlops? Spearmint: 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 Spearmint and awesome-mlops? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Spearmint trust report](/tools/hips-spearmint/trust); [awesome-mlops trust report](/tools/visenger-awesome-mlops/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=hips-spearmint`](/api/graphcanon/graph?tool=hips-spearmint) - 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/_