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Comparison

Made-With-ML vs mlflow

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 mlflow if mLflow is an open-source platform for managing the lifecycle of AI and ML models, offering vendor-neutral capabilities for debugging, evaluating, monitoring, and optimizing models across different environments.

Markdown twin · Made-With-ML alternatives · mlflow alternatives

GraphCanon updated Sep 20, 2026

10views this month

Made-With-ML logo

Made-With-ML

GokuMohandas/Made-With-ML

★ 50kpushed Mar 4, 2026
vs
mlflow logo

mlflow

mlflow/mlflow

★ 28kpushed Sep 18, 2026

Trust & integrity

SignalMade-With-MLmlflow
Maintenance
Slowing (199d since push)
As of Sep 20, 2026 · github_public_v1
Very active (0d since push)
As of Sep 18, 2026 · github_public_v1
Provenance
Not a fork · Personal account
As of Sep 20, 2026 · github_public_v1
Not a fork · Organization account
As of Sep 18, 2026 · github_public_v1
OSV dependency advisories
Published findings
As of Jul 15, 2026 · osv@v1
No lockfile (source not queried)
As of Sep 18, 2026 · osv@v1
deps.dev advisories
Not queried
deps.dev@v1
Not queried
deps.dev@v1
OpenSSF Scorecard
Not queried
openssf-scorecard@v1
Not queried
openssf-scorecard@v1
Made-With-ML trust reportmlflow trust reportTrust methodology

Tagline

Made-With-ML
Learn to develop, deploy and iterate on production-grade ML applications
mlflow
Open source AI engineering platform for agents, LLMs, and ML models

Stars

Made-With-ML
50k
mlflow
28k

Forks

Made-With-ML
7.8k
mlflow
6.3k

Open issues

Made-With-ML
25
mlflow
2.1k

Language

Made-With-ML
Jupyter Notebook
mlflow
Python

Adopt for

Made-With-ML
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.
mlflow
MLflow is an open-source platform for managing the lifecycle of AI and ML models, offering vendor-neutral capabilities for debugging, evaluating, monitoring, and optimizing models across different environments.

Persona

Made-With-ML
-
mlflow
-

Runtime

Made-With-ML
-
mlflow
-

License

Made-With-ML
MIT
mlflow
Apache-2.0

Last pushed

Made-With-ML
Mar 4, 2026
mlflow
Sep 18, 2026

Categories

Made-With-ML
Developer Tools, Inference & Serving, Model Training
mlflow
Developer Tools, Evaluation & Observability, Inference & Serving, Model Training

Trust and health

Maintenance

Made-With-ML
Slowing (36%)
mlflow
Very active (96%)

Days since push

Made-With-ML
199d
mlflow
0d

Open issues (now)

Made-With-ML
25
mlflow
2.1k

Stars delta

Made-With-ML
+473 (30d)
mlflow
+895 (30d)

Open issues delta

Made-With-ML
-1 (30d)
mlflow
+31 (30d)

Owner type

Made-With-ML
User
mlflow
Organization

OSV dependency advisories

Made-With-ML
Published findings
mlflow
No lockfile (source not queried)

Full report

Made-With-ML
Trust report
mlflow
Trust report

Choose Made-With-ML if…

  • Made-With-ML is primarily Jupyter Notebook; mlflow is Python.
  • License: Made-With-ML is MIT, mlflow is Apache-2.0.
  • Requirements: A foundational understanding of Python programming is required to fully benefit from the learning resources provided..
  • Tags unique to Made-With-ML: data-engineering, data-quality, data-science, deep-learning.
  • If you are looking for comprehensive tutorials that connect foundational ML concepts directly with hands-on coding practices using Python and PyTorch.

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.
GitHubDetails

Choose mlflow if…

  • mlflow is primarily Python; Made-With-ML is Jupyter Notebook.
  • License: mlflow is Apache-2.0, Made-With-ML is MIT.
  • Tags unique to mlflow: agentops, agents, ai-governance, langchain.
  • Also covers Evaluation & Observability.
  • When you need a vendor-neutral solution for managing the lifecycle of AI and ML models, including debugging, evaluation, monitoring, and optimization.

When NOT to use mlflow

  • If you are looking for a platform that is tightly integrated with a specific vendor's ecosystem, as MLflow's vendor-neutral approach may not leverage vendor-specific optimizations.
  • When your project strictly requires proprietary features or services that are not available in open-source platforms like MLflow.
  • If your team prefers a more specialized tool for a specific aspect of AI/ML lifecycle management, as MLflow is a general-purpose platform and may not offer the depth of features in a niche area.
GitHubDetails

Explore

Made-With-ML trust report →mlflow trust report →Developer Tools category →Inference & Serving category →Model Training category →Evaluation & Observability category →All comparisonsStack workflowsTrending tools

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: Made-With-ML 50k · mlflow 28k (synced Sep 20, 2026).

Common questions

What is the difference between Made-With-ML and mlflow?
Made-With-ML: Learn to develop, deploy and iterate on production-grade ML applications. mlflow: Open source AI engineering platform for agents, LLMs, and ML models. See the comparison table for live GitHub stats and shared categories.
When should I choose Made-With-ML over mlflow?
Choose Made-With-ML over mlflow when Made-With-ML is primarily Jupyter Notebook; mlflow is Python; License: Made-With-ML is MIT, mlflow is Apache-2.0; Requirements: A foundational understanding of Python programming is required to fully benefit from the learning resources provided.; Tags unique to Made-With-ML: data-engineering, data-quality, data-science, deep-learning; 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 mlflow over Made-With-ML?
Choose mlflow over Made-With-ML when mlflow is primarily Python; Made-With-ML is Jupyter Notebook; License: mlflow is Apache-2.0, Made-With-ML is MIT; Tags unique to mlflow: agentops, agents, ai-governance, langchain; Also covers Evaluation & Observability; When you need a vendor-neutral solution for managing the lifecycle of AI and ML models, including debugging, evaluation, monitoring, and optimization.
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 mlflow?
If you are looking for a platform that is tightly integrated with a specific vendor's ecosystem, as MLflow's vendor-neutral approach may not leverage vendor-specific optimizations. When your project strictly requires proprietary features or services that are not available in open-source platforms like MLflow. If your team prefers a more specialized tool for a specific aspect of AI/ML lifecycle management, as MLflow is a general-purpose platform and may not offer the depth of features in a niche area.
Is Made-With-ML or mlflow more popular on GitHub?
Made-With-ML has more GitHub stars (49,547 vs 28,010). Stars measure visibility, not whether either tool fits your constraints.
Are Made-With-ML and mlflow open source?
Yes - both are open-source projects on GitHub (Made-With-ML: MIT, mlflow: Apache-2.0).
Where can I find alternatives to Made-With-ML or mlflow?
GraphCanon lists graph-backed alternatives at Made-With-ML alternatives and mlflow alternatives (Made-With-ML markdown twin, mlflow markdown twin), 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 mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
Which is better maintained, Made-With-ML or mlflow?
Made-With-ML: Slowing. mlflow: 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 Made-With-ML and mlflow?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Made-With-ML trust report; mlflow trust report.

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