---
title: "awesome-production-machine-learning vs redis-ai-resources"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/ethicalml-awesome-production-machine-learning-vs-redis-developer-redis-ai-resources"
tools: ["ethicalml-awesome-production-machine-learning", "redis-developer-redis-ai-resources"]
---

# awesome-production-machine-learning vs redis-ai-resources

*GraphCanon updated Jul 11, 2026*

## Verdict

Pick awesome-production-machine-learning when tags unique to awesome-production-machine-learning: awesome, deep-learning, data-mining, large-scale-ml; pick redis-ai-resources when tags unique to redis-ai-resources: vector-database, ai, feature-store, 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. [redis-ai-resources](https://github.com/redis-developer/redis-ai-resources) has 473 stars, 74 forks, and 14 open issues, last pushed Jul 10, 2026. Figures are from public GitHub metadata via [awesome-production-machine-learning's repository](https://github.com/EthicalML/awesome-production-machine-learning) and [redis-ai-resources's repository](https://github.com/redis-developer/redis-ai-resources).

| | [awesome-production-machine-learning](/tools/ethicalml-awesome-production-machine-learning.md) | [redis-ai-resources](/tools/redis-developer-redis-ai-resources.md) |
| --- | --- | --- |
| Tagline | A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning | ✨ A curated list of awesome community resources, integrations, and examples of Redis in the AI ecosystem. |
| Stars | 20,719 | 473 |
| Forks | 2,585 | 74 |
| Open issues | 32 | 14 |
| Language | - | Jupyter Notebook |
| Adopt for | - | - |
| Persona | - | - |
| Runtime | - | - |
| License | MIT | MIT |
| Categories | LLM Frameworks, AI Agents, Vector Databases | Vector Databases |

## Trust and health

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

| | [awesome-production-machine-learning](/tools/ethicalml-awesome-production-machine-learning.md) | [redis-ai-resources](/tools/redis-developer-redis-ai-resources.md) |
| --- | --- | --- |
| Maintenance | Active (82%) | Very active (96%) |
| Days since push | 8d | 0d |
| Open issues (now) | 32 | 14 |
| Full report | [trust report](/tools/ethicalml-awesome-production-machine-learning/trust.md) | [trust report](/tools/redis-developer-redis-ai-resources/trust.md) |

## Choose when

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

- Tags unique to awesome-production-machine-learning: awesome, deep-learning, data-mining, large-scale-ml.
- Also covers LLM Frameworks, AI Agents.
- More GitHub stars (21k vs 473) - visibility, not fit.

### Choose redis-ai-resources if…

- Tags unique to redis-ai-resources: vector-database, ai, feature-store, machine-learning.
- More recently updated (last pushed Jul 10, 2026).

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

- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
- AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism.
- 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 redis-ai-resources

- Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.

## Common questions

### What is the difference between awesome-production-machine-learning and redis-ai-resources?

awesome-production-machine-learning: A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning. redis-ai-resources: ✨ A curated list of awesome community resources, integrations, and examples of Redis in the AI ecosystem.. See the comparison table for live GitHub stats and shared categories.

### When should I choose awesome-production-machine-learning over redis-ai-resources?

Choose awesome-production-machine-learning over redis-ai-resources when Tags unique to awesome-production-machine-learning: awesome, deep-learning, data-mining, large-scale-ml; Also covers LLM Frameworks, AI Agents; More GitHub stars (21k vs 473) - visibility, not fit.

### When should I choose redis-ai-resources over awesome-production-machine-learning?

Choose redis-ai-resources over awesome-production-machine-learning when Tags unique to redis-ai-resources: vector-database, ai, feature-store, machine-learning; More recently updated (last pushed Jul 10, 2026).

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

LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. 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 redis-ai-resources?

Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.

### Is awesome-production-machine-learning or redis-ai-resources more popular on GitHub?

awesome-production-machine-learning has more GitHub stars (20,719 vs 473). Stars measure visibility, not whether either tool fits your constraints.

### Are awesome-production-machine-learning and redis-ai-resources open source?

Yes - both are open-source projects on GitHub (awesome-production-machine-learning: MIT, redis-ai-resources: MIT).

### Where can I find alternatives to awesome-production-machine-learning or redis-ai-resources?

GraphCanon lists graph-backed alternatives at [awesome-production-machine-learning alternatives](/tools/ethicalml-awesome-production-machine-learning/alternatives) and [redis-ai-resources alternatives](/tools/redis-developer-redis-ai-resources/alternatives) ([awesome-production-machine-learning markdown twin](/tools/ethicalml-awesome-production-machine-learning/alternatives.md), [redis-ai-resources markdown twin](/tools/redis-developer-redis-ai-resources/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-redis-developer-redis-ai-resources.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 redis-ai-resources?

awesome-production-machine-learning: Active. redis-ai-resources: 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 awesome-production-machine-learning and redis-ai-resources?

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); [redis-ai-resources trust report](/tools/redis-developer-redis-ai-resources/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/_