Comparison
Made-With-ML vs mlem
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 mlem if mLEM is a Python-based tool that streamlines packaging, serving, and deploying machine learning models across different platforms via CLI.
Markdown twin · Made-With-ML alternatives · mlem alternatives
GraphCanon updated 1w
vs
Trust & integrity
| Signal | Made-With-ML | mlem |
|---|---|---|
| Maintenance | Slowing (162d since push) As of 1w · github_public_v1 | Archived (1055d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 1w · github_public_v1 | Not a fork · Organization account As of 3w · github_public_v1 |
| OSV dependency advisories | Published findings As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · 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 |
Tagline
- Made-With-ML
- Learn to develop, deploy and iterate on production-grade ML applications
- mlem
- A tool to package, serve, and deploy any ML model on any platform.
Stars
- Made-With-ML
- 49k
- mlem
- 718
Forks
- Made-With-ML
- 7.7k
- mlem
- 42
Open issues
- Made-With-ML
- 26
- mlem
- 131
Language
- Made-With-ML
- Jupyter Notebook
- mlem
- 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.
- mlem
- MLEM is a Python-based tool that streamlines packaging, serving, and deploying machine learning models across different platforms via CLI.
Persona
- Made-With-ML
- -
- mlem
- -
Runtime
- Made-With-ML
- -
- mlem
- -
License
- Made-With-ML
- MIT
- mlem
- Apache-2.0
Last pushed
- Made-With-ML
- Mar 4, 2026
- mlem
- Sep 13, 2023
Categories
- Made-With-ML
- Developer Tools, Inference & Serving, Model Training
- mlem
- Developer Tools, Inference & Serving
Trust and health
Maintenance
- Made-With-ML
- Slowing (36%)
- mlem
- Archived (8%)
Days since push
- Made-With-ML
- 162d
- mlem
- 1055d
Archived on GitHub
- Made-With-ML
- No
- mlem
- Yes
Open issues (now)
- Made-With-ML
- 26
- mlem
- 131
Stars delta
- Made-With-ML
- +371 (30d)
- mlem
- Unknown
Open issues delta
- Made-With-ML
- -1 (30d)
- mlem
- Unknown
Owner type
- Made-With-ML
- User
- mlem
- Organization
OSV dependency advisories
- Made-With-ML
- Published findings
- mlem
- No lockfile (source not queried)
Full report
- Made-With-ML
- Trust report
- mlem
- Trust report
Shared compatibility
- Python · Made-With-ML: Python runtime · mlem: Python runtime
Choose Made-With-ML if…
- Made-With-ML is primarily Jupyter Notebook; mlem is Python.
- License: Made-With-ML is MIT, mlem 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, deep-learning, distributed-ml.
- Also covers Model Training.
- 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.
Choose mlem if…
- mlem is primarily Python; Made-With-ML is Jupyter Notebook.
- License: mlem is Apache-2.0, Made-With-ML is MIT.
- Tags unique to mlem: cli, deployment, git, model-registry.
- Use MLEM if you are looking to deploy ML models quickly using a command-line interface (CLI), making it ideal for teams preferring script-driven integration.
When NOT to use mlem
- Avoid MLEM if you are working in environments where strict package dependency management is required outside Python, as it might complicate integration with non-Python native services.
- If detailed manual configuration of deployment settings is a necessity for your application, consider alternatives that offer more granular control over model serving parameters and configurations.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (GokuMohandas/Made-With-ML) · observed Aug 14, 2026
- GitHub forks (GokuMohandas/Made-With-ML) · observed Aug 14, 2026
- Last push (GokuMohandas/Made-With-ML) · observed Mar 4, 2026
- License file (MIT) · observed Aug 14, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
- GitHub stars (iterative/mlem) · observed Aug 4, 2026
- GitHub forks (iterative/mlem) · observed Aug 4, 2026
- Last push (iterative/mlem) · observed Sep 13, 2023
- License file (Apache-2.0) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Made-With-ML 49k · mlem 718 (synced Aug 14, 2026).
Common questions
- What is the difference between Made-With-ML and mlem?
- Made-With-ML: Learn to develop, deploy and iterate on production-grade ML applications. mlem: A tool to package, serve, and deploy any ML model on any platform.. See the comparison table for live GitHub stats and shared categories.
- When should I choose Made-With-ML over mlem?
- Choose Made-With-ML over mlem when Made-With-ML is primarily Jupyter Notebook; mlem is Python; License: Made-With-ML is MIT, mlem 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, deep-learning, distributed-ml; Also covers Model Training; 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 mlem over Made-With-ML?
- Choose mlem over Made-With-ML when mlem is primarily Python; Made-With-ML is Jupyter Notebook; License: mlem is Apache-2.0, Made-With-ML is MIT; Tags unique to mlem: cli, deployment, git, model-registry; Use MLEM if you are looking to deploy ML models quickly using a command-line interface (CLI), making it ideal for teams preferring script-driven integration.
- 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 mlem?
- Avoid MLEM if you are working in environments where strict package dependency management is required outside Python, as it might complicate integration with non-Python native services. If detailed manual configuration of deployment settings is a necessity for your application, consider alternatives that offer more granular control over model serving parameters and configurations.
- Is Made-With-ML or mlem more popular on GitHub?
- Made-With-ML has more GitHub stars (49,074 vs 718). Stars measure visibility, not whether either tool fits your constraints.
- Are Made-With-ML and mlem open source?
- Yes - both are open-source projects on GitHub (Made-With-ML: MIT, mlem: Apache-2.0).
- Where can I find alternatives to Made-With-ML or mlem?
- GraphCanon lists graph-backed alternatives at Made-With-ML alternatives and mlem alternatives (Made-With-ML markdown twin, mlem 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 mlem?
- Made-With-ML: Slowing. mlem: Archived. 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 mlem?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Made-With-ML trust report; mlem trust report.