SE-ML/awesome-seml resource

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1367 stars · 124 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A curated list of articles, papers, and guides covering software engineering best practices for building machine learning applications, spanning data management, model training, deployment, governance, and team collaboration. It accompanies a survey on the adoption of these practices in industry.

Use cases

• find best practices for building production ML applications
• learn MLOps and software engineering for machine learning
• find must-read papers on ML technical debt and engineering
• learn how to manage datasets and data quality for ML
• find resources on testing and versioning ML models
• onboard a team to ML engineering practices

When to choose

• you want a curated, categorized reading list on ML engineering practices
• you need academic papers and industry guides on MLOps topics
• you are setting up team processes for ML development

When to avoid

• you need runnable tools or code rather than articles and papers
• you want tutorials on ML algorithms themselves rather than surrounding engineering
• you need an up-to-date tooling directory rather than conceptual guidance

Facets

learning-resource · maturity active

documentation developer-tools machine-learning awesome-lists developer-tools awesome-list mlops curated-list best-practices software-engineering ml-engineering devops web-server

Member repositories

For agents

markdown · JSON · MCP: product_card(name="SE-ML/awesome-seml")