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

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

*GraphCanon updated Sep 20, 2026*

## Verdict

Pick machine-learning-systems-design if a booklet designed to provide an overview of machine learning systems design, featuring hands-on exercises and practical resources; pick awesome-mlops if awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML.

[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-mlops](https://github.com/kelvins/awesome-mlops) has 5.3k stars, 775 forks, and 82 open issues, last pushed Aug 17, 2026. Figures are from public GitHub metadata via [machine-learning-systems-design's repository](https://github.com/chiphuyen/machine-learning-systems-design) and [awesome-mlops's repository](https://github.com/kelvins/awesome-mlops).

| | [machine-learning-systems-design](/tools/chiphuyen-machine-learning-systems-design.md) | [awesome-mlops](/tools/kelvins-awesome-mlops.md) |
| --- | --- | --- |
| Tagline | A booklet on machine learning systems design with exercises | A curated list of awesome MLOps tools. |
| Stars | 10,601 | 5,265 |
| Forks | 1,635 | 775 |
| Open issues | 11 | 82 |
| Language | HTML | Python |
| Adopt for | A booklet designed to provide an overview of machine learning systems design, featuring hands-on exercises and practical resources. | Awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML. |
| Persona | developer harness | - |
| Runtime | - | - |
| License | License information is unavailable. | - |
| Categories | Data & Retrieval, Developer Tools, Evaluation & Observability, Inference & Serving, Model Training | Developer Tools, Evaluation & Observability, Inference & Serving, Model Training |

## Trust and health

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

| | [machine-learning-systems-design](/tools/chiphuyen-machine-learning-systems-design.md) | [awesome-mlops](/tools/kelvins-awesome-mlops.md) |
| --- | --- | --- |
| Maintenance | Dormant (18%) | Active (82%) |
| Days since push | 1253d | 18d |
| Open issues (now) | 11 | 82 |
| Stars delta | +92 (30d) | +36 (30d) |
| Open issues delta | 0 (30d) | +11 (30d) |
| Full report | [trust report](/tools/chiphuyen-machine-learning-systems-design/trust.md) | [trust report](/tools/kelvins-awesome-mlops/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-mlops

- **Adopt for:** Awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML.

## Choose when

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

- machine-learning-systems-design is primarily HTML; awesome-mlops is Python.
- 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: machine-learning-production.
- Also covers Data & Retrieval.
- Use for a quick initial introduction to the key aspects of ML system design if you are unfamiliar with the foundational concepts.

### Choose awesome-mlops if…

- awesome-mlops is primarily Python; machine-learning-systems-design is HTML.
- Tags unique to awesome-mlops: ai, awesome, machine-learning, machine-learning-engineering.
- You need resources across multiple facets of the machine-learning pipeline, from data validation to model serving.

## 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-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 machine-learning-systems-design and awesome-mlops?

machine-learning-systems-design: A booklet on machine learning systems design with exercises. awesome-mlops: A curated list of awesome MLOps tools.. See the comparison table for live GitHub stats and shared categories.

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

Choose machine-learning-systems-design over awesome-mlops when machine-learning-systems-design is primarily HTML; awesome-mlops is Python; 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: machine-learning-production; Also covers Data & Retrieval; 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-mlops over machine-learning-systems-design?

Choose awesome-mlops over machine-learning-systems-design when awesome-mlops is primarily Python; machine-learning-systems-design is HTML; Tags unique to awesome-mlops: ai, awesome, machine-learning, machine-learning-engineering; You need resources across multiple facets of the machine-learning pipeline, from data validation to model serving.

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

machine-learning-systems-design has more GitHub stars (10,601 vs 5,265). Stars measure visibility, not whether either tool fits your constraints.

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

Yes - both are open-source projects on GitHub.

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

GraphCanon lists graph-backed alternatives at [machine-learning-systems-design alternatives](/tools/chiphuyen-machine-learning-systems-design/alternatives) and [awesome-mlops alternatives](/tools/kelvins-awesome-mlops/alternatives) ([machine-learning-systems-design markdown twin](/tools/chiphuyen-machine-learning-systems-design/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/chiphuyen-machine-learning-systems-design-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, machine-learning-systems-design or awesome-mlops?

machine-learning-systems-design: Dormant. awesome-mlops: 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-mlops?

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-mlops trust report](/tools/kelvins-awesome-mlops/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/_