End to End toy example of MLOps

Overview

churn_model

MLOps Toy Example End to End

You might find below links useful

  1. Connect VSCode to Git
  2. MLFlow Port
  3. Heroku App

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- A default Sphinx project; see sphinx-doc.org for details
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to download or generate data
│   │   └── make_dataset.py
│   │
│   ├── features       <- Scripts to turn raw data into features for modeling
│   │   └── build_features.py
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions
│   │   ├── predict_model.py
│   │   └── train_model.py
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       └── visualize.py
│
└── tox.ini            <- tox file with settings for running tox; see tox.readthedocs.io

Project based on the cookiecutter data science project template. #cookiecutterdatascience

Owner
Ashish Tele
Senior Data Scientist at Cardinal Health, Data Science graduate from UConn School of Business
Ashish Tele
Machine Learning for RC Cars

Suiron Machine Learning for RC Cars Prediction visualization (green = actual, blue = prediction) Click the video below to see it in action! Dependenci

Kendrick Tan 706 Jan 02, 2023
Python Automated Machine Learning library for tabular data.

Simple but powerful Automated Machine Learning library for tabular data. It uses efficient in-memory SAP HANA algorithms to automate routine Data Scie

Daniel Khromov 47 Dec 17, 2022
AutoTabular automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications.

AutoTabular AutoTabular automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just

wenqi 2 Jun 26, 2022
Machine Learning from Scratch

Machine Learning from Scratch Author: Shengxuan Wang From: Oregon State University Content: Building Machine Learning model from Scratch, without usin

ShawnWang 0 Jul 05, 2022
mlpack: a scalable C++ machine learning library --

a fast, flexible machine learning library Home | Documentation | Doxygen | Community | Help | IRC Chat Download: current stable version (3.4.2) mlpack

mlpack 4.2k Jan 01, 2023
The MLOps is the process of continuous integration and continuous delivery of Machine Learning artifacts as a software product, keeping it inside a loop of Design, Model Development and Operations.

MLOps The MLOps is the process of continuous integration and continuous delivery of Machine Learning artifacts as a software product, keeping it insid

Maykon Schots 25 Nov 27, 2022
Simplify stop motion animation with machine learning.

Simplify stop motion animation with machine learning.

Nick Bild 25 Sep 15, 2022
A library of extension and helper modules for Python's data analysis and machine learning libraries.

Mlxtend (machine learning extensions) is a Python library of useful tools for the day-to-day data science tasks. Sebastian Raschka 2014-2021 Links Doc

Sebastian Raschka 4.2k Dec 29, 2022
All-in-one web-based development environment for machine learning

All-in-one web-based development environment for machine learning Getting Started • Features & Screenshots • Support • Report a Bug • FAQ • Known Issu

3 Feb 03, 2021
Production Grade Machine Learning Service

This project is made to help you scale from a basic Machine Learning project for research purposes to a production grade Machine Learning web service

Abdullah Zaiter 10 Apr 04, 2022
A Time Series Library for Apache Spark

Flint: A Time Series Library for Apache Spark The ability to analyze time series data at scale is critical for the success of finance and IoT applicat

Two Sigma 970 Jan 04, 2023
The easy way to combine mlflow, hydra and optuna into one machine learning pipeline.

mlflow_hydra_optuna_the_easy_way The easy way to combine mlflow, hydra and optuna into one machine learning pipeline. Objective TODO Usage 1. build do

shibuiwilliam 9 Sep 09, 2022
This is a Cricket Score Predictor that predicts the first innings score of a T20 Cricket match using Machine Learning

This is a Cricket Score Predictor that predicts the first innings score of a T20 Cricket match using Machine Learning. It is a Web Application.

Developer Junaid 3 Aug 04, 2022
AutoTabular automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications.

AutoTabular automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy m

Robin 55 Dec 27, 2022
TIANCHI Purchase Redemption Forecast Challenge

TIANCHI Purchase Redemption Forecast Challenge

Haorui HE 4 Aug 26, 2022
CrayLabs and user contibuted examples of using SmartSim for various simulation and machine learning applications.

SmartSim Example Zoo This repository contains CrayLabs and user contibuted examples of using SmartSim for various simulation and machine learning appl

Cray Labs 14 Mar 30, 2022
Automated Machine Learning with scikit-learn

auto-sklearn auto-sklearn is an automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator. Find the documentation here

AutoML-Freiburg-Hannover 6.7k Jan 07, 2023
Toolss - Automatic installer of hacking tools (ONLY FOR TERMUKS!)

Tools Автоматический установщик хакерских утилит (ТОЛЬКО ДЛЯ ТЕРМУКС!) Оригиналь

14 Jan 05, 2023
Python package for stacking (machine learning technique)

vecstack Python package for stacking (stacked generalization) featuring lightweight functional API and fully compatible scikit-learn API Convenient wa

Igor Ivanov 671 Dec 25, 2022
Decision tree is the most powerful and popular tool for classification and prediction

Diabetes Prediction Using Decision Tree Introduction Decision tree is the most powerful and popular tool for classification and prediction. A Decision

Arjun U 1 Jan 23, 2022