---
title: "machine-learning-systems-design vs awesome-production-machine-learning"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/chiphuyen-machine-learning-systems-design-vs-ethicalml-awesome-production-machine-learning"
tools: ["chiphuyen-machine-learning-systems-design", "ethicalml-awesome-production-machine-learning"]
---

# machine-learning-systems-design vs awesome-production-machine-learning

*GraphCanon updated Sep 20, 2026*

## Verdict

Pick machine-learning-systems-design when pricing: Free to use, no charge for the booklet but additional content like answers to practice questions may be contained in a book that entails a cost.; pick awesome-production-machine-learning when tags unique to awesome-production-machine-learning: inference-serving, machine-learning-operations, ml-ops, model-deployment.

[machine-learning-systems-design](https://huyenchip.com/machine-learning-systems-design/toc.html) reports 11k GitHub stars, 1.6k forks, and 11 open issues, last pushed Apr 15, 2023. [awesome-production-machine-learning](https://ethicalml.github.io/awesome-production-machine-learning) has 21k stars, 2.6k forks, and 32 open issues, last pushed Sep 3, 2026. Figures are from public GitHub metadata via [machine-learning-systems-design's repository](https://github.com/chiphuyen/machine-learning-systems-design) and [awesome-production-machine-learning's repository](https://github.com/EthicalML/awesome-production-machine-learning).

| | [machine-learning-systems-design](/tools/chiphuyen-machine-learning-systems-design.md) | [awesome-production-machine-learning](/tools/ethicalml-awesome-production-machine-learning.md) |
| --- | --- | --- |
| Tagline | A booklet on machine learning systems design with exercises | A curated list of awesome open source libraries for deploying, monitoring, versioning and scaling machine learning |
| Stars | 10,601 | 20,891 |
| Forks | 1,635 | 2,598 |
| Open issues | 11 | 32 |
| Language | HTML | - |
| Adopt for | A booklet designed to provide an overview of machine learning systems design, featuring hands-on exercises and practical resources. | - |
| Persona | developer harness | - |
| Runtime | - | - |
| License | License information is unavailable. | MIT license making it free for use in both personal and commercial projects without requiring royalty payment or source-code disclosure. |
| Categories | Data & Retrieval, Developer Tools, Evaluation & Observability, Inference & Serving, Model Training | Data & Retrieval, Evaluation & Observability, Inference & Serving |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [machine-learning-systems-design](/tools/chiphuyen-machine-learning-systems-design.md) | [awesome-production-machine-learning](/tools/ethicalml-awesome-production-machine-learning.md) |
| --- | --- | --- |
| Maintenance | Dormant (18%) | Very active (96%) |
| Days since push | 1253d | 0d |
| Open issues (now) | 11 | 32 |
| Stars delta | +92 (30d) | +70 (30d) |
| Open issues delta | 0 (30d) | +1 (30d) |
| Owner type | User | Organization |
| Full report | [trust report](/tools/chiphuyen-machine-learning-systems-design/trust.md) | [trust report](/tools/ethicalml-awesome-production-machine-learning/trust.md) |

## Decision facts: machine-learning-systems-design

- **Pricing:** freemium - Free to use, no charge for the booklet but additional content like answers to practice questions may be contained in a book that entails a cost.
- **Adopt for:** A booklet designed to provide an overview of machine learning systems design, featuring hands-on exercises and practical resources.
- **License detail:** License information is unavailable.
- **Persona:** developer harness

## 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.

## Choose when

### Choose machine-learning-systems-design if…

- Pricing: Free to use, no charge for the booklet but additional content like answers to practice questions may be contained in a book that entails a cost..
- Tags unique to machine-learning-systems-design: data-science, machine-learning-production, mlops.
- Also covers Developer Tools, Model Training.
- Use for a quick initial introduction to the key aspects of ML system design if you are unfamiliar with the foundational concepts.

### Choose awesome-production-machine-learning if…

- Tags unique to awesome-production-machine-learning: inference-serving, machine-learning-operations, ml-ops, model-deployment.
- If you need a diverse set of open-source tools for end-to-end production machine learning tasks
- More GitHub stars (21k vs 11k) - visibility, not fit.

## When NOT to use machine-learning-systems-design

- Not recommended if you require an exhaustive guide; this booklet has been superseded by a more comprehensive book 'Designing Machine Learning Systems'.
- Avoid using solely as the basis for designing production-ready machine learning systems without further reading and validation from current industry standards or more recent resources.

## 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

## Common questions

### What is the difference between machine-learning-systems-design and awesome-production-machine-learning?

machine-learning-systems-design: A booklet on machine learning systems design with exercises. awesome-production-machine-learning: A curated list of awesome open source libraries for deploying, monitoring, versioning and scaling machine learning. See the comparison table for live GitHub stats and shared categories.

### When should I choose machine-learning-systems-design over awesome-production-machine-learning?

Choose machine-learning-systems-design over awesome-production-machine-learning when Pricing: Free to use, no charge for the booklet but additional content like answers to practice questions may be contained in a book that entails a cost.; Tags unique to machine-learning-systems-design: data-science, machine-learning-production, mlops; Also covers Developer Tools, Model Training; Use for a quick initial introduction to the key aspects of ML system design if you are unfamiliar with the foundational concepts.

### When should I choose awesome-production-machine-learning over machine-learning-systems-design?

Choose awesome-production-machine-learning over machine-learning-systems-design when Tags unique to awesome-production-machine-learning: inference-serving, machine-learning-operations, ml-ops, model-deployment; If you need a diverse set of open-source tools for end-to-end production machine learning tasks; More GitHub stars (21k vs 11k) - visibility, not fit.

### When should I avoid machine-learning-systems-design?

Not recommended if you require an exhaustive guide; this booklet has been superseded by a more comprehensive book 'Designing Machine Learning Systems'. Avoid using solely as the basis for designing production-ready machine learning systems without further reading and validation from current industry standards or more recent resources.

### 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

### Is machine-learning-systems-design or awesome-production-machine-learning more popular on GitHub?

awesome-production-machine-learning has more GitHub stars (20,891 vs 10,601). Stars measure visibility, not whether either tool fits your constraints.

### Are machine-learning-systems-design and awesome-production-machine-learning open source?

Yes - both are open-source projects on GitHub.

### Where can I find alternatives to machine-learning-systems-design or awesome-production-machine-learning?

GraphCanon lists graph-backed alternatives at [machine-learning-systems-design alternatives](/tools/chiphuyen-machine-learning-systems-design/alternatives) and [awesome-production-machine-learning alternatives](/tools/ethicalml-awesome-production-machine-learning/alternatives) ([machine-learning-systems-design markdown twin](/tools/chiphuyen-machine-learning-systems-design/alternatives.md), [awesome-production-machine-learning markdown twin](/tools/ethicalml-awesome-production-machine-learning/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/chiphuyen-machine-learning-systems-design-vs-ethicalml-awesome-production-machine-learning.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, machine-learning-systems-design or awesome-production-machine-learning?

machine-learning-systems-design: Dormant. awesome-production-machine-learning: 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 machine-learning-systems-design and awesome-production-machine-learning?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [machine-learning-systems-design trust report](/tools/chiphuyen-machine-learning-systems-design/trust); [awesome-production-machine-learning trust report](/tools/ethicalml-awesome-production-machine-learning/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=chiphuyen-machine-learning-systems-design`](/api/graphcanon/graph?tool=chiphuyen-machine-learning-systems-design)
- 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/_