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
☆103Updated 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☆160Updated last year
- ☆32Updated 2 years ago
- Deploy A/B testing infrastructure in a containerized microservice architecture for Machine Learning applications.☆40Updated 5 months ago
- Main folder. Material related to my books on synthetic data and generative AI. Also contains documents blending components from several f…☆95Updated 9 months ago
- Slides for "Feature engineering for time series forecasting" talk☆60Updated 2 years ago
- Python Feature Engineering Cookbook, Third Edition, published by Packt☆55Updated last month
- Demo for CI/CD in a machine learning project☆106Updated last year
- Awesome MLOps Course Outline☆34Updated 2 years ago
- Machine Learning Model Serving Patterns and Best Practices☆35Updated last year
- Machine Learning for Streaming Data with Python, published by Packt☆71Updated last year
- The repository contains a list of projects which I will work on while learning and implementing MLOps.☆78Updated 2 years ago
- Machine Learning Engineering with MLflow, published by Packt☆115Updated 11 months ago
- An end-to-end project on customer segmentation☆81Updated 2 years ago
- Examples of using Evidently to evaluate, test and monitor ML models.☆30Updated last week
- Develop and deploy a real-time feature pipeline in Python, using Bytewax 🐝 and Hopsworks Feature Store.☆135Updated last year
- Machine Learning Ops Project☆29Updated last year
- Find data quality issues and clean your data in a single line of code with a Scikit-Learn compatible Transformer.☆130Updated last year
- Learn how to monitor ML systems to identify and mitigate sources of drift before model performance decay.☆86Updated 2 years ago
- Learn to build a modular real-time feature pipeline, so you avoid Offline-Online Feature Skew, and your deployed ML models work as expect…☆44Updated last year
- Fetch, transform and plot real-time OHLC data from Coinbase using Bytewax, Bokeh and Streamlit☆129Updated last year
- MLOps maturity assessment☆60Updated 2 years ago
- This repository contains the material (datasets, code, videos, spreadsheets) related to my book Stochastic Processes and Simulations - A …☆50Updated 2 years ago
- Applied Machine Learning Explainability Techniques, published by Packt☆247Updated last year
- A book of subtle code tricks and gem resources for all things data, machine learning and deep learning.☆166Updated 9 months ago
- Exercises performed as part of the ML Zoomcamp course☆30Updated 3 years ago
- Test LLMs automatically with Giskard and CI/CD☆30Updated 10 months ago
- Using a feature store to connect the DataOps and MLOps workflows to enable collaborative teams to develop efficiently.☆56Updated 2 years ago
- Code Repository for The Kaggle Workbook, Published by Packt☆121Updated last month
- ☆27Updated last year
- Free Open-source ML observability course for data scientists and ML engineers. Learn how to monitor and debug your ML models in productio…☆89Updated last year