ml-for-high-risk-apps-book / Machine-Learning-for-High-Risk-Applications-BookLinks
Official code repo for the O'Reilly Book - Machine Learning for High-Risk Applications
☆105Updated 2 years ago
Alternatives and similar repositories for Machine-Learning-for-High-Risk-Applications-Book
Users that are interested in Machine-Learning-for-High-Risk-Applications-Book are comparing it to the libraries listed below
Sorting:
- Practical Deep Learning at Scale with MLFlow, published by Packt☆163Updated this week
- Learn how to monitor ML systems to identify and mitigate sources of drift before model performance decay.☆96Updated 3 years ago
- Machine Learning for Streaming Data with Python, published by Packt☆73Updated last month
- Applied Machine Learning Explainability Techniques, published by Packt☆248Updated this week
- Learn how to create reliable ML systems by testing code, data and models.☆91Updated 3 years ago
- Code Repository for The Kaggle Workbook, Published by Packt☆137Updated this week
- Main folder. Material related to my books on synthetic data and generative AI. Also contains documents blending components from several f…☆100Updated last year
- A book of subtle code tricks and gem resources for all things data, machine learning and deep learning.☆170Updated last year
- Demo for CI/CD in a machine learning project☆115Updated 2 years ago
- An end-to-end project on customer segmentation☆83Updated 3 years ago
- Slides for "Feature engineering for time series forecasting" talk☆66Updated 3 years ago
- Implementation of various Machine learning and MLOps applications/tutorials used within my Medium blog.☆11Updated 3 years ago
- Deploy A/B testing infrastructure in a containerized microservice architecture for Machine Learning applications.☆40Updated last year
- Comet for Data Science, published by Packt☆42Updated this week
- Machine Learning Engineering with Python☆187Updated this week
- Machine Learning Model Serving Patterns and Best Practices☆36Updated this week
- Production-Ready Applied Deep Learning☆90Updated this week
- Reference code base for ML Engineering, Manning Publications☆137Updated 4 years ago
- Develop and deploy a real-time feature pipeline in Python, using Bytewax 🐝 and Hopsworks Feature Store.☆135Updated 2 years ago
- Using a feature store to connect the DataOps and MLOps workflows to enable collaborative teams to develop efficiently.☆57Updated 3 years ago
- ☆20Updated 3 years ago
- Engineering MLOps, published by Packt☆191Updated this week
- Find data quality issues and clean your data in a single line of code with a Scikit-Learn compatible Transformer.☆135Updated 2 years ago
- Tutorials on creating a reproducible and maintainable data science project☆149Updated 3 years ago
- Adding timestamps to NumFOCUS and PyData YouTube videos!☆106Updated 3 years ago
- Interpretable ML with Python, 2E - published by Packt☆107Updated 3 months ago
- Fetch, transform and plot real-time OHLC data from Coinbase using Bytewax, Bokeh and Streamlit☆128Updated last year
- MLOps maturity assessment☆62Updated 2 years ago
- ☆12Updated 2 years ago
- ☆295Updated 2 years ago