---
title: "awesome-production-machine-learning vs what_are_embeddings"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/ethicalml-awesome-production-machine-learning-vs-veekaybee-what-are-embeddings"
tools: ["ethicalml-awesome-production-machine-learning", "veekaybee-what-are-embeddings"]
---

# awesome-production-machine-learning vs what_are_embeddings

*GraphCanon updated Jul 12, 2026*

## Verdict

Pick awesome-production-machine-learning when tags unique to awesome-production-machine-learning: awesome, awesome-list, data-mining, deep-learning; pick what_are_embeddings when tags unique to what_are_embeddings: embeddings, machine-learning-algorithms, nlp-machine-learning.

[awesome-production-machine-learning](https://ethicalml.github.io/awesome-production-machine-learning) reports 21k GitHub stars, 2.6k forks, and 32 open issues, last pushed Jul 3, 2026. [what_are_embeddings](http://vickiboykis.com/what_are_embeddings/) has 1.1k stars, 87 forks, and 0 open issues, last pushed Jan 17, 2026. Figures are from public GitHub metadata via [awesome-production-machine-learning's repository](https://github.com/EthicalML/awesome-production-machine-learning) and [what_are_embeddings's repository](https://github.com/veekaybee/what_are_embeddings).

| | [awesome-production-machine-learning](/tools/ethicalml-awesome-production-machine-learning.md) | [what_are_embeddings](/tools/veekaybee-what-are-embeddings.md) |
| --- | --- | --- |
| Tagline | A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning | A deep dive into embeddings starting from fundamentals |
| Stars | 20,719 | 1,091 |
| Forks | 2,585 | 87 |
| Open issues | 32 | 0 |
| Language | - | Jupyter Notebook |
| Adopt for | - | Focuses on educational materials for understanding embeddings in ML and NLP using Jupyter Notebooks. |
| Persona | - | - |
| Runtime | - | - |
| License | MIT | - |
| Categories | AI Agents, LLM Frameworks, Vector Databases | Data & Retrieval |

## Trust and health

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

| | [awesome-production-machine-learning](/tools/ethicalml-awesome-production-machine-learning.md) | [what_are_embeddings](/tools/veekaybee-what-are-embeddings.md) |
| --- | --- | --- |
| Maintenance | Active (82%) | Slowing (36%) |
| Days since push | 8d | 175d |
| Open issues (now) | 32 | 0 |
| Owner type | Organization | User |
| Full report | [trust report](/tools/ethicalml-awesome-production-machine-learning/trust.md) | [trust report](/tools/veekaybee-what-are-embeddings/trust.md) |

## Decision facts: what_are_embeddings

- **Adopt for:** Focuses on educational materials for understanding embeddings in ML and NLP using Jupyter Notebooks.

## Choose when

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

- Tags unique to awesome-production-machine-learning: awesome, awesome-list, data-mining, deep-learning.
- Also covers AI Agents, LLM Frameworks, Vector Databases.
- More GitHub stars (21k vs 1.1k) - visibility, not fit.

### Choose what_are_embeddings if…

- Tags unique to what_are_embeddings: embeddings, machine-learning-algorithms, nlp-machine-learning.
- Also covers Data & Retrieval.
- When you are looking to gain foundational knowledge about how embeddings work in machine learning and natural language processing tasks.

## When NOT to use awesome-production-machine-learning

- AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism.
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
- Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.

## When NOT to use what_are_embeddings

- If you need practical, real-world application examples or code implementations not grounded in explanatory educational content.
- When an advanced understanding of embeddings is required as this repository prioritizes fundamental comprehension over deep technical insights.

## Common questions

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

awesome-production-machine-learning: A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning. what_are_embeddings: A deep dive into embeddings starting from fundamentals. See the comparison table for live GitHub stats and shared categories.

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

Choose awesome-production-machine-learning over what_are_embeddings when Tags unique to awesome-production-machine-learning: awesome, awesome-list, data-mining, deep-learning; Also covers AI Agents, LLM Frameworks, Vector Databases; More GitHub stars (21k vs 1.1k) - visibility, not fit.

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

Choose what_are_embeddings over awesome-production-machine-learning when Tags unique to what_are_embeddings: embeddings, machine-learning-algorithms, nlp-machine-learning; Also covers Data & Retrieval; When you are looking to gain foundational knowledge about how embeddings work in machine learning and natural language processing tasks.

### When should I avoid awesome-production-machine-learning?

AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.

### When should I avoid what_are_embeddings?

If you need practical, real-world application examples or code implementations not grounded in explanatory educational content. When an advanced understanding of embeddings is required as this repository prioritizes fundamental comprehension over deep technical insights.

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

awesome-production-machine-learning has more GitHub stars (20,719 vs 1,091). Stars measure visibility, not whether either tool fits your constraints.

### Are awesome-production-machine-learning and what_are_embeddings open source?

Yes - both are open-source projects on GitHub.

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

GraphCanon lists graph-backed alternatives at [awesome-production-machine-learning alternatives](/tools/ethicalml-awesome-production-machine-learning/alternatives) and [what_are_embeddings alternatives](/tools/veekaybee-what-are-embeddings/alternatives) ([awesome-production-machine-learning markdown twin](/tools/ethicalml-awesome-production-machine-learning/alternatives.md), [what_are_embeddings markdown twin](/tools/veekaybee-what-are-embeddings/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-veekaybee-what-are-embeddings.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 what_are_embeddings?

awesome-production-machine-learning: Active. what_are_embeddings: Slowing. 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 what_are_embeddings?

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); [what_are_embeddings trust report](/tools/veekaybee-what-are-embeddings/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/_