--- title: "awesome-production-machine-learning vs GSM-IC" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/ethicalml-awesome-production-machine-learning-vs-google-research-datasets-gsm-ic" tools: ["ethicalml-awesome-production-machine-learning", "google-research-datasets-gsm-ic"] --- # awesome-production-machine-learning vs GSM-IC *GraphCanon updated Oct 4, 2026* ## Verdict Pick awesome-production-machine-learning when tags unique to awesome-production-machine-learning: inference-serving, machine-learning-operations, ml-ops, model-deployment; pick GSM-IC when pricing: Pricing details are not available.. [awesome-production-machine-learning](https://ethicalml.github.io/awesome-production-machine-learning) reports 21k GitHub stars, 2.6k forks, and 37 open issues, last pushed Oct 3, 2026. [GSM-IC](https://github.com/google-research-datasets/GSM-IC) has 67 stars, 2 forks, and 1 open issues, last pushed Feb 13, 2023. Figures are from public GitHub metadata via [awesome-production-machine-learning's repository](https://github.com/EthicalML/awesome-production-machine-learning) and [GSM-IC's repository](https://github.com/google-research-datasets/GSM-IC). | | [awesome-production-machine-learning](/tools/ethicalml-awesome-production-machine-learning.md) | [GSM-IC](/tools/google-research-datasets-gsm-ic.md) | | --- | --- | --- | | Tagline | A curated list of awesome open source libraries for deploying, monitoring, versioning and scaling machine learning | GSM-IC benchmark evaluates distractibility of language models with grade-school math problems and irrelevant context. | | Stars | 20,969 | 67 | | Forks | 2,607 | 2 | | Open issues | 37 | 1 | | Language | - | - | | Adopt for | - | GSM-IC is a benchmark dataset that evaluates the distractibility of language models by introducing irrelevant context into grade-school math problems. | | Persona | - | - | | Runtime | - | - | | License | MIT license making it free for use in both personal and commercial projects without requiring royalty payment or source-code disclosure. | License information is not provided in the repository. | | Categories | Data & Retrieval, Evaluation & Observability, Inference & Serving | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-production-machine-learning](/tools/ethicalml-awesome-production-machine-learning.md) | [GSM-IC](/tools/google-research-datasets-gsm-ic.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Archived (8%) | | Days since push | 0d | 1312d | | Archived on GitHub | No | Yes | | Open issues (now) | 37 | 1 | | Stars delta | +148 (30d) | 0 (30d) | | Open issues delta | +6 (30d) | 0 (30d) | | Full report | [trust report](/tools/ethicalml-awesome-production-machine-learning/trust.md) | [trust report](/tools/google-research-datasets-gsm-ic/trust.md) | ## Decision facts: awesome-production-machine-learning - **License detail:** MIT license making it free for use in both personal and commercial projects without requiring royalty payment or source-code disclosure. ## Decision facts: GSM-IC - **Pricing:** unknown - Pricing details are not available. - **Requirements:** The dataset is derived from GSM8K and includes irrelevant context in problem descriptions to test the distractibility of language models. - **Adopt for:** GSM-IC is a benchmark dataset that evaluates the distractibility of language models by introducing irrelevant context into grade-school math problems. - **License detail:** License information is not provided in the repository. ## Choose when ### Choose awesome-production-machine-learning if… - Tags unique to awesome-production-machine-learning: inference-serving, machine-learning-operations, ml-ops, model-deployment. - Also covers Data & Retrieval, Inference & Serving. - If you need a diverse set of open-source tools for end-to-end production machine learning tasks ### Choose GSM-IC if… - Pricing: Pricing details are not available.. - Requirements: The dataset is derived from GSM8K and includes irrelevant context in problem descriptions to test the distractibility of language models.. - Tags unique to GSM-IC: arithmetic reasoning, benchmark, distractibility, grade-school math. - Use GSM-IC when you need to assess how well a language model can focus on relevant information in arithmetic reasoning tasks, especially when faced with distracting irrelevant context. ## When NOT to use awesome-production-machine-learning - If you seek a comprehensive solution integrated into one platform rather than selecting from diverse tools - When your project is specific to only one aspect of machine learning like just deployment or monitoring, and not for the entire workflow - For teams preferring vendor-specific solutions over open-source options ## When NOT to use GSM-IC - Avoid GSM-IC if your evaluation focuses on the model's ability to solve math problems without any form of distraction or if the context is always assumed to be relevant. - Do not use GSM-IC if your primary interest is in evaluating the model's performance on pure arithmetic tasks without the added complexity of irrelevant context. ## Common questions ### What is the difference between awesome-production-machine-learning and GSM-IC? awesome-production-machine-learning: A curated list of awesome open source libraries for deploying, monitoring, versioning and scaling machine learning. GSM-IC: GSM-IC benchmark evaluates distractibility of language models with grade-school math problems and irrelevant context.. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-production-machine-learning over GSM-IC? Choose awesome-production-machine-learning over GSM-IC when Tags unique to awesome-production-machine-learning: inference-serving, machine-learning-operations, ml-ops, model-deployment; Also covers Data & Retrieval, Inference & Serving; If you need a diverse set of open-source tools for end-to-end production machine learning tasks. ### When should I choose GSM-IC over awesome-production-machine-learning? Choose GSM-IC over awesome-production-machine-learning when Pricing: Pricing details are not available.; Requirements: The dataset is derived from GSM8K and includes irrelevant context in problem descriptions to test the distractibility of language models.; Tags unique to GSM-IC: arithmetic reasoning, benchmark, distractibility, grade-school math; Use GSM-IC when you need to assess how well a language model can focus on relevant information in arithmetic reasoning tasks, especially when faced with distracting irrelevant context. ### When should I avoid awesome-production-machine-learning? If you seek a comprehensive solution integrated into one platform rather than selecting from diverse tools When your project is specific to only one aspect of machine learning like just deployment or monitoring, and not for the entire workflow For teams preferring vendor-specific solutions over open-source options ### When should I avoid GSM-IC? Avoid GSM-IC if your evaluation focuses on the model's ability to solve math problems without any form of distraction or if the context is always assumed to be relevant. Do not use GSM-IC if your primary interest is in evaluating the model's performance on pure arithmetic tasks without the added complexity of irrelevant context. ### Is awesome-production-machine-learning or GSM-IC more popular on GitHub? awesome-production-machine-learning has more GitHub stars (20,969 vs 67). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-production-machine-learning and GSM-IC open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to awesome-production-machine-learning or GSM-IC? GraphCanon lists graph-backed alternatives at [awesome-production-machine-learning alternatives](/tools/ethicalml-awesome-production-machine-learning/alternatives) and [GSM-IC alternatives](/tools/google-research-datasets-gsm-ic/alternatives) ([awesome-production-machine-learning markdown twin](/tools/ethicalml-awesome-production-machine-learning/alternatives.md), [GSM-IC markdown twin](/tools/google-research-datasets-gsm-ic/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/ethicalml-awesome-production-machine-learning-vs-google-research-datasets-gsm-ic.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, awesome-production-machine-learning or GSM-IC? awesome-production-machine-learning: Very active. GSM-IC: Archived. 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-production-machine-learning and GSM-IC? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-production-machine-learning trust report](/tools/ethicalml-awesome-production-machine-learning/trust); [GSM-IC trust report](/tools/google-research-datasets-gsm-ic/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=ethicalml-awesome-production-machine-learning`](/api/graphcanon/graph?tool=ethicalml-awesome-production-machine-learning) - 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/_