Alternatives and similar repositories for software_engineering_for_data_scientists:

Users that are interested in software_engineering_for_data_scientists are comparing it to the libraries listed below

• ml-for-high-risk-apps-book / Machine-Learning-for-High-Risk-Applications-Book
  Official code repo for the O'Reilly Book - Machine Learning for High-Risk Applications
  ☆101Updated last year
• PacktPublishing / Machine-Learning-for-Streaming-Data-with-Python
  Machine Learning for Streaming Data with Python, published by Packt
  ☆68Updated last year
• solegalli / machine-learning-interpretability
  Code repository for the online course Machine Learning Interpretability
  ☆23Updated 4 months ago
• trainindata / forecasting-with-machine-learning
  Code repository for the course "Forecasting with Machine Learning Models"
  ☆18Updated 8 months ago
• manuelgilm / mlflow_for_ml_dev
  Repository with code examples of mlflow
  ☆67Updated this week
• srivatsan88 / google_cloud_notebooks
  ☆13Updated 4 years ago
• PacktPublishing / Time-Series-Analysis-with-Python-Cookbook-Second-Edition
  Time Series Analysis with Python Cookbook, Second Edition - Published by Packt
  ☆34Updated 2 weeks ago
• PacktPublishing / Data-Cleaning-and-Exploration-with-Machine-Learning
  Data Cleaning and Exploration with Machine Learning
  ☆53Updated 2 years ago
• PacktPublishing / Cleaning-Data-for-Effective-Data-Science
  Cleaning Data for Effective Data Science, published by Packt
  ☆96Updated 2 years ago
• PacktWorkshops / The-Deep-Learning-with-PyTorch-Workshop
  ☆33Updated last year
• PacktPublishing / Python-Feature-Engineering-Cookbook-Second-Edition
  Python Feature Engineering Cookbook Second Edition, published by Packt
  ☆80Updated last year
• kwangyy / midterm-project
  ☆12Updated 3 years ago
• PacktPublishing / Practical-Deep-Learning-at-Scale-with-MLFlow
  Practical Deep Learning at Scale with MLFlow, published by Packt
  ☆159Updated last year
• PacktPublishing / Machine-Learning-Model-Serving-Patterns-and-Best-Practices
  Machine Learning Model Serving Patterns and Best Practices
  ☆35Updated last year
• PacktPublishing / The-Kaggle-Workbook
  Code Repository for The Kaggle Workbook, Published by Packt
  ☆118Updated last year
• solegalli / feature-selection-in-machine-learning-book
  Code repository for the book feature selection in machine learning
  ☆27Updated 2 weeks ago
• BexTuychiev / medium_stories
  A repository containing data and files for my stories on Medium.com.
  ☆56Updated 2 weeks ago
• KishManani / PyDataLondon2022
  Slides for "Feature engineering for time series forecasting" talk
  ☆58Updated 2 years ago
• PacktPublishing / Comet-for-Data-Science
  Comet for Data Science, published by Packt
  ☆42Updated last year
• data-max-hq / ab-testing-in-ml
  Deploy A/B testing infrastructure in a containerized microservice architecture for Machine Learning applications.
  ☆40Updated last month
• KishManani / m5-forecasting-tutorial
  An end-to-end tutorial to forecast the M5 dataset using feature engineering pipelines and gradient boosting.
  ☆16Updated 2 years ago
• ahmedbesbes / mlflow
  Introduction to MLflow with a demo locally and how to set it on AWS
  ☆42Updated 3 years ago
• solegalli / feature_engine
  Feature engineering package with sklearn like functionality
  ☆52Updated 5 months ago
• ysraell / forecasting
  Forecasting: Principles and Practice
  ☆30Updated 3 years ago
• kennethleungty / End-to-End-AutoML-Insurance
  An End-to-End Implementation of AutoML with H2O, MLflow, FastAPI, and Streamlit for Insurance Cross-Sell
  ☆75Updated 2 years ago
• PacktPublishing / Debugging-Machine-Learning-Models-with-Python
  Debugging Machine Learning Models with Python, published by Packt
  ☆54Updated 3 weeks ago
• DataTalksClub / reading-club-nlp
  Notes from our NLP reading club!
  ☆16Updated 3 years ago
• Ksyula / ML_Engineering
  Machine Learning Engineering Camp 2022
  ☆40Updated 2 years ago
• outerbounds / dsbook
  Code samples for the Effective Data Science Infrastructure book
  ☆113Updated last year
• DrDub / artfeateng
  Code for the book Art of Feature Engineering
  ☆31Updated 3 years ago