LDSSA / heroku-model-deploy
Deploy a scikit model using heroku and Flask
☆14Updated last year
Related projects ⓘ
Alternatives and complementary repositories for heroku-model-deploy
- this repo might get accepted☆29Updated 3 years ago
- A few baselines with a standard tabular model☆38Updated 4 years ago
- General Interpretability Package☆58Updated last year
- ☆16Updated 3 years ago
- The fast.ai data ethics course☆14Updated last year
- Embed categorical variables via neural networks.☆59Updated last year
- Python implementation of R package breakDown☆41Updated last year
- Guide for applying Unit Testing in data-driven projects☆19Updated 4 years ago
- Powerful rapid automatic EDA and feature engineering library with a very easy to use API 🌟☆53Updated 2 years ago
- Train multi-task image, text, or ensemble (image + text) models☆45Updated last year
- Python package for Bayesian Tests / AB Testing☆40Updated 4 years ago
- Material for PyCon 2019 NLP Tutorial☆33Updated 5 years ago
- Scripts for paper "Encoding high-cardinality string categorical variables"☆24Updated 5 years ago
- Repo for work on deep learning for tabular data☆14Updated 4 years ago
- Tutorial for a new versioning Machine Learning pipeline☆81Updated 3 years ago
- A scikit-learn compatible estimator based on business-rules with interactive dashboard included☆28Updated 3 years ago
- Practical ideas on securing machine learning models☆36Updated 3 years ago
- CinnaMon is a Python library which offers a number of tools to detect, explain, and correct data drift in a machine learning system☆76Updated last year
- Small Dataset Benchmarks on the Dataset of Datasets UCI++☆85Updated 2 years ago
- Tabular feature encoding pipelines for machine learning with options for string parsing, missing data infill, and stochastic perturbation…☆165Updated 2 months ago
- The stand-alone training engine module for the ALOHA.eu project.☆15Updated 5 years ago
- A lightweight command line interface for the management of arbitrary machine learning tasks☆19Updated 3 years ago
- A machine learning testing framework for sklearn and pandas. The goal is to help folks assess whether things have changed over time.☆101Updated 3 years ago
- Content for the Model Interpretability Tutorial at Pycon US 2019☆41Updated 3 months ago
- A minimal template for creating a pypi package☆49Updated 3 years ago
- A practical, explainable and effective method for reducing bias in machine learning algorithms.☆21Updated 4 years ago
- ☆30Updated 5 years ago
- ☆14Updated 5 years ago