Made-With-ML
Learn to develop, deploy and iterate on production-grade ML applications
GraphCanon updated Aug 14, 2026 · GitHub synced Aug 14, 2026
62views this month
Decision brief
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.
Good fit when
- If you are looking for comprehensive tutorials that connect foundational ML concepts directly with hands-on coding practices using Python and PyTorch.
- If scaling your ML workloads is a concern and you prefer to do so entirely in Python, avoiding the need to learn new languages or systems.
Avoid when
- 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.
- Requirements:
- A foundational understanding of Python programming is required to fully benefit from the learning resources provided.
Observed Jul 17, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Slowing (162d since push)
- As of Aug 14, 2026
- Provenance
- Not a fork · Personal account
- As of Aug 14, 2026
- Security (OSV)
- 262 low (262 low)
- As of Jul 15, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/GokuMohandas/Made-With-MLHow it fits your stack(8)
Typed graph edges - alternatives, integrations, successors, and dependencies. Ranked by relationship type, not raw GitHub stars.
Integrates
Related
Relationship graph
Optional deeper exploration of typed edges and category neighbours.
Similar tools
Same-category neighbours not already linked as typed edges.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Provides learning resources for designing, developing, deploying, and iterating production-grade machine learning applications.
Capability facts
- Languages
- jupyter notebook, python
Source: github.language+pyproject.toml · Aug 14, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 14, 2026)
- **📈 Scale**: easily scale ML workloads (data, train, tune, serve) in Python without having to learn completely new languages.Source link
Tags
README
<div align="center" <h1 <img width="30" src="https://madewithml.com/static/images/rounded logo.png" <a href="https://madewithml.com/" Made With ML</a </h1 Design · Develop · Deploy · Iterate <br Join 40K+ developers in learning how to responsibly deliver value with ML. <br...
For agents
This page has a .md twin and JSON over the API.