Project Title
applied-ml โ Curated Resources for Applied Data Science and Machine Learning in Production
Overview
Applied-ml is a comprehensive repository that serves as a go-to resource for professionals and enthusiasts in the field of data science and machine learning. It offers a curated collection of papers, articles, and blogs from various companies, detailing their experiences and insights on implementing machine learning in production environments. This project stands out for its focus on real-world applications and the practical challenges faced during the deployment of ML models.
Key Features
- Extensive collection of resources on data science and machine learning in production.
- Organized by topics, including data quality, engineering, discovery, and various ML techniques.
- Summaries of ML advancements and real-world results for better ROI assessment.
Use Cases
- Data scientists looking for case studies and research papers to improve their understanding of ML in production.
- ML engineers seeking practical insights on implementing and managing machine learning models.
- Researchers and academics needing up-to-date references and literature for their studies.
Advantages
- Provides a centralized platform for accessing a wide range of resources on applied machine learning.
- Offers insights into how different organizations frame problems and the techniques they use.
- Facilitates learning from the successes and failures of other companies in deploying ML models.
Limitations / Considerations
- The project relies on external content, which may not always be up-to-date or comprehensive.
- The effectiveness of the resources depends on the reader's ability to apply the insights in their specific context.
- The project does not provide hands-on tools or software for machine learning implementation.
Similar / Related Projects
- ml-surveys: A collection of machine learning surveys and papers, focusing more on academic overviews rather than practical applications.
- awesome-machine-learning: A broad list of machine learning resources, including books, videos, and articles, but less focused on production applications.
- data-science-blogs: A list of data science blogs, which may include some overlap with applied-ml but is not as curated or focused on production.
Basic Information
- GitHub: https://github.com/eugeneyan/applied-ml
- Stars: 28,234
- License: Unknown
- Last Commit: 2025-08-20
๐ Project Information
- Project Name: applied-ml
- GitHub URL: https://github.com/eugeneyan/applied-ml
- Programming Language: Unknown
- โญ Stars: 28,234
- ๐ด Forks: 3,781
- ๐ Created: 2020-07-04
- ๐ Last Updated: 2025-08-20
๐ท๏ธ Project Topics
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๐ Related Resource Links
๐ Documentation
๐ฅ Video Tutorials
- An Approach to Data Quality for Netflix Personalization Systems
- Zipline: Airbnbโs Machine Learning Data Management Platform
- Sputnik: Airbnbโs Apache Spark Framework for Data Engineering
- Zipline - A Declarative Feature Engineering Framework
๐ Related Websites
This article is automatically generated by AI based on GitHub project information and README content analysis