---
title: "Made-With-ML vs awesome-mlops"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/gokumohandas-made-with-ml-vs-kelvins-awesome-mlops"
tools: ["gokumohandas-made-with-ml", "kelvins-awesome-mlops"]
---

# Made-With-ML vs awesome-mlops

*GraphCanon updated Aug 14, 2026*

## Verdict

Pick Made-With-ML if made-With-ML is about equipping developers with practical resources to design, develop, deploy and iterate on production-grade machine learning applications within their software engineering workflows; pick awesome-mlops if awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML.

[Made-With-ML](https://madewithml.com) reports 49k GitHub stars, 7.7k forks, and 26 open issues, last pushed Mar 4, 2026. [awesome-mlops](https://github.com/kelvins/awesome-mlops) has 5.2k stars, 762 forks, and 71 open issues, last pushed Apr 29, 2026. Figures are from public GitHub metadata via [Made-With-ML's repository](https://github.com/GokuMohandas/Made-With-ML) and [awesome-mlops's repository](https://github.com/kelvins/awesome-mlops).

| | [Made-With-ML](/tools/gokumohandas-made-with-ml.md) | [awesome-mlops](/tools/kelvins-awesome-mlops.md) |
| --- | --- | --- |
| Tagline | Learn to develop, deploy and iterate on production-grade ML applications | A curated list of awesome MLOps tools. |
| Stars | 49,074 | 5,229 |
| Forks | 7,710 | 762 |
| Open issues | 26 | 71 |
| Language | Jupyter Notebook | Python |
| Adopt for | Made-With-ML is about equipping developers with practical resources to design, develop, deploy and iterate on production-grade machine learning applications within their software engineering workflows. | Awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML. |
| Persona | - | - |
| Runtime | - | - |
| License | MIT | - |
| Categories | Developer Tools, Inference & Serving, Model Training | Developer Tools, Evaluation & Observability, Inference & Serving, Model Training |

## Trust and health

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

| | [Made-With-ML](/tools/gokumohandas-made-with-ml.md) | [awesome-mlops](/tools/kelvins-awesome-mlops.md) |
| --- | --- | --- |
| Days since push | 162d | 97d |
| Open issues (now) | 26 | 71 |
| Stars delta | +371 (30d) | Unknown |
| Open issues delta | -1 (30d) | Unknown |
| Full report | [trust report](/tools/gokumohandas-made-with-ml/trust.md) | [trust report](/tools/kelvins-awesome-mlops/trust.md) |

**Typed relationship:** Made-With-ML _(related)_ awesome-mlops

Made-With-ML and awesome-mlops both deal with the deployment, monitoring, and scaling of machine learning applications.

## Shared compatibility

- **Python**: [Made-With-ML](/tools/gokumohandas-made-with-ml.md) - Python runtime; [awesome-mlops](/tools/kelvins-awesome-mlops.md) - Python runtime

## Decision facts: Made-With-ML

- **Requirements:** A foundational understanding of Python programming is required to fully benefit from the learning resources provided.
- **Adopt for:** Made-With-ML is about equipping developers with practical resources to design, develop, deploy and iterate on production-grade machine learning applications within their software engineering workflows.

## Decision facts: awesome-mlops

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

## Choose when

### Choose Made-With-ML if…

- Made-With-ML is primarily Jupyter Notebook; awesome-mlops is Python.
- Requirements: A foundational understanding of Python programming is required to fully benefit from the learning resources provided..
- Made-With-ML and awesome-mlops both deal with the deployment, monitoring, and scaling of machine learning applications.
- Tags unique to Made-With-ML: data-engineering, data-quality, deep-learning, distributed-ml.
- If you are looking for comprehensive tutorials that connect foundational ML concepts directly with hands-on coding practices using Python and PyTorch.

### Choose awesome-mlops if…

- awesome-mlops is primarily Python; Made-With-ML is Jupyter Notebook.
- Made-With-ML and awesome-mlops both deal with the deployment, monitoring, and scaling of machine learning applications.
- Tags unique to awesome-mlops: ai, awesome, machine-learning-engineering, ml.
- Also covers Evaluation & Observability.
- You need resources across multiple facets of the machine-learning pipeline, from data validation to model serving.

## When NOT to use Made-With-ML

- If you are looking for a niche-focused tool that caters specifically to a particular machine learning framework other than PyTorch.
- For developers who already have strong backgrounds in MLOps and require highly specialized tools for managing production-grade ML deployments without additional educational support.

## 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 Made-With-ML and awesome-mlops?

Made-With-ML: Learn to develop, deploy and iterate on production-grade ML applications. awesome-mlops: A curated list of awesome MLOps tools.. See the comparison table for live GitHub stats and shared categories.

### When should I choose Made-With-ML over awesome-mlops?

Choose Made-With-ML over awesome-mlops when Made-With-ML is primarily Jupyter Notebook; awesome-mlops is Python; Requirements: A foundational understanding of Python programming is required to fully benefit from the learning resources provided.; Made-With-ML and awesome-mlops both deal with the deployment, monitoring, and scaling of machine learning applications; Tags unique to Made-With-ML: data-engineering, data-quality, deep-learning, distributed-ml; If you are looking for comprehensive tutorials that connect foundational ML concepts directly with hands-on coding practices using Python and PyTorch.

### When should I choose awesome-mlops over Made-With-ML?

Choose awesome-mlops over Made-With-ML when awesome-mlops is primarily Python; Made-With-ML is Jupyter Notebook; Made-With-ML and awesome-mlops both deal with the deployment, monitoring, and scaling of machine learning applications; Tags unique to awesome-mlops: ai, awesome, machine-learning-engineering, ml; Also covers Evaluation & Observability; You need resources across multiple facets of the machine-learning pipeline, from data validation to model serving.

### When should I avoid Made-With-ML?

If you are looking for a niche-focused tool that caters specifically to a particular machine learning framework other than PyTorch. For developers who already have strong backgrounds in MLOps and require highly specialized tools for managing production-grade ML deployments without additional educational support.

### 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 Made-With-ML or awesome-mlops more popular on GitHub?

Made-With-ML has more GitHub stars (49,074 vs 5,229). Stars measure visibility, not whether either tool fits your constraints.

### Are Made-With-ML and awesome-mlops open source?

Yes - both are open-source projects on GitHub.

### Where can I find alternatives to Made-With-ML or awesome-mlops?

GraphCanon lists graph-backed alternatives at [Made-With-ML alternatives](/tools/gokumohandas-made-with-ml/alternatives) and [awesome-mlops alternatives](/tools/kelvins-awesome-mlops/alternatives) ([Made-With-ML markdown twin](/tools/gokumohandas-made-with-ml/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/gokumohandas-made-with-ml-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, Made-With-ML or awesome-mlops?

Made-With-ML: Slowing. awesome-mlops: 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 Made-With-ML and awesome-mlops?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Made-With-ML trust report](/tools/gokumohandas-made-with-ml/trust); [awesome-mlops trust report](/tools/kelvins-awesome-mlops/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=gokumohandas-made-with-ml`](/api/graphcanon/graph?tool=gokumohandas-made-with-ml)
- 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/_