Comparison
Made-With-ML vs llm-course
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 llm-course if llm-course provides a comprehensive curriculum on Large Language Models, including fundamental knowledge, building and deploying LLMs, and hands-on Colab notebooks.
Markdown twin · Made-With-ML alternatives · llm-course alternatives
GraphCanon updated Sep 20, 2026
6views this month
vs
Trust & integrity
| Signal | Made-With-ML | llm-course |
|---|---|---|
| Maintenance | Slowing (199d since push) As of Sep 20, 2026 · github_public_v1 | Slowing (224d 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 · Personal 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 |
Tagline
- Made-With-ML
- Learn to develop, deploy and iterate on production-grade ML applications
- llm-course
- Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
Stars
- Made-With-ML
- 50k
- llm-course
- 83k
Forks
- Made-With-ML
- 7.8k
- llm-course
- 9.7k
Open issues
- Made-With-ML
- 25
- llm-course
- 90
Language
- Made-With-ML
- Jupyter Notebook
- llm-course
- -
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.
- llm-course
- llm-course provides a comprehensive curriculum on Large Language Models, including fundamental knowledge, building and deploying LLMs, and hands-on Colab notebooks.
Persona
- Made-With-ML
- -
- llm-course
- -
Runtime
- Made-With-ML
- -
- llm-course
- -
License
- Made-With-ML
- MIT
- llm-course
- Apache-2.0
Last pushed
- Made-With-ML
- Mar 4, 2026
- llm-course
- Feb 5, 2026
Categories
- Made-With-ML
- Developer Tools, Inference & Serving, Model Training
- llm-course
- Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training
Trust and health
Days since push
- Made-With-ML
- 199d
- llm-course
- 224d
Open issues (now)
- Made-With-ML
- 25
- llm-course
- 90
Stars delta
- Made-With-ML
- +473 (30d)
- llm-course
- +1.5k (30d)
Open issues delta
- Made-With-ML
- -1 (30d)
- llm-course
- +4 (30d)
OSV dependency advisories
- Made-With-ML
- Published findings
- llm-course
- No lockfile (source not queried)
Full report
- Made-With-ML
- Trust report
- llm-course
- Trust report
Shared compatibility
- Python · Made-With-ML: Python runtime · llm-course: Python runtime
Choose Made-With-ML if…
- License: Made-With-ML is MIT, llm-course 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.
Choose llm-course if…
- License: llm-course is Apache-2.0, Made-With-ML is MIT.
- Tags unique to llm-course: course, large-language-models, llm, roadmap.
- Also covers Evaluation & Observability, LLM Frameworks.
- Use llm-course if you are looking for a structured learning path that includes both theoretical and practical aspects of LLMs, from fundamentals to deployment.
When NOT to use llm-course
- Avoid llm-course if you are seeking a course that focuses solely on theoretical aspects without practical implementation.
- Do not use llm-course if you prefer a more formal certification program or a course that is part of a university curriculum.
- Skip llm-course if you are looking for a tool that provides only code snippets or pre-built models without a structured learning path.
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 Sep 20, 2026
- GitHub forks (GokuMohandas/Made-With-ML) · observed Sep 20, 2026
- Last push (GokuMohandas/Made-With-ML) · observed Mar 4, 2026
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
- GitHub stars (mlabonne/llm-course) · observed Sep 20, 2026
- GitHub forks (mlabonne/llm-course) · observed Sep 20, 2026
- Last push (mlabonne/llm-course) · observed Feb 5, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
GitHub stars on cards: Made-With-ML 50k · llm-course 83k (synced Sep 20, 2026).
Common questions
- What is the difference between Made-With-ML and llm-course?
- Made-With-ML: Learn to develop, deploy and iterate on production-grade ML applications. llm-course: Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.. See the comparison table for live GitHub stats and shared categories.
- When should I choose Made-With-ML over llm-course?
- Choose Made-With-ML over llm-course when License: Made-With-ML is MIT, llm-course 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 llm-course over Made-With-ML?
- Choose llm-course over Made-With-ML when License: llm-course is Apache-2.0, Made-With-ML is MIT; Tags unique to llm-course: course, large-language-models, llm, roadmap; Also covers Evaluation & Observability, LLM Frameworks; Use llm-course if you are looking for a structured learning path that includes both theoretical and practical aspects of LLMs, from fundamentals to deployment.
- 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 llm-course?
- Avoid llm-course if you are seeking a course that focuses solely on theoretical aspects without practical implementation. Do not use llm-course if you prefer a more formal certification program or a course that is part of a university curriculum. Skip llm-course if you are looking for a tool that provides only code snippets or pre-built models without a structured learning path.
- Is Made-With-ML or llm-course more popular on GitHub?
- llm-course has more GitHub stars (83,011 vs 49,547). Stars measure visibility, not whether either tool fits your constraints.
- Are Made-With-ML and llm-course open source?
- Yes - both are open-source projects on GitHub (Made-With-ML: MIT, llm-course: Apache-2.0).
- Where can I find alternatives to Made-With-ML or llm-course?
- GraphCanon lists graph-backed alternatives at Made-With-ML alternatives and llm-course alternatives (Made-With-ML markdown twin, llm-course 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 llm-course?
- Made-With-ML: Slowing. llm-course: 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 llm-course?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Made-With-ML trust report; llm-course trust report.