machine learning model deployment project of Iris classification model in a minimal UI using flask web framework and deployed it in Azure cloud using Azure app service

Overview

Machine Learning Model Deployment

Iris Classification

Hello everyone, this is a machine learning model deployment project where we have presented the Iris classification model in an elegant basic minimal ui using flask web framework and deployed it in Azure cloud using Azure app service. We initially made the notebook file, with model code and some data preperations (preprocessing). Then we have took some chunks of code and put the necessary in web project. Then we have deployed it into Azure app service. We initially made this project as a requirement for an internship at Indian Servers. We are now making it open to contribution.

Authors :

Tejaswi Talluru - Machine Learning Model.
Krishna Priyatham Potluri - flask web project and Azure deployment.

Step by step guide to run, contribute, test in local machine.

  1. clone this repo into desired location in your system using command: git clone https://github.com/kittupriyatham/Machine-Learning-Model-Deployment.git
  2. Go to the project folder using command: cd Machine-Learning-Model-Deployment
  3. Initialize local repository using command: git init
  4. create a python virtual environment using command: python -m venv venv
  5. activate the virtual environment using command: venv\Scripts\activate
  6. Install the project dependancies using command: pip install -r requirements.txt
  7. launch the flask server using command: flask run

Screenshots of deployed project

image

image

image

Steps to Deploy in Azure App Service

To be updated soon...

Kats is a toolkit to analyze time series data, a lightweight, easy-to-use, and generalizable framework to perform time series analysis.

Kats, a kit to analyze time series data, a lightweight, easy-to-use, generalizable, and extendable framework to perform time series analysis, from understanding the key statistics and characteristics

Facebook Research 4.1k Dec 29, 2022
LILLIE: Information Extraction and Database Integration Using Linguistics and Learning-Based Algorithms

LILLIE: Information Extraction and Database Integration Using Linguistics and Learning-Based Algorithms Based on the work by Smith et al. (2021) Query

5 Aug 06, 2022
Xeasy-ml is a packaged machine learning framework.

xeasy-ml 1. What is xeasy-ml Xeasy-ml is a packaged machine learning framework. It allows a beginner to quickly build a machine learning model and use

9 Mar 14, 2022
DirectML is a high-performance, hardware-accelerated DirectX 12 library for machine learning.

DirectML is a high-performance, hardware-accelerated DirectX 12 library for machine learning. DirectML provides GPU acceleration for common machine learning tasks across a broad range of supported ha

Microsoft 1.1k Jan 04, 2023
Combines MLflow with a database (PostgreSQL) and a reverse proxy (NGINX) into a multi-container Docker application

Combines MLflow with a database (PostgreSQL) and a reverse proxy (NGINX) into a multi-container Docker application (with docker-compose).

Philip May 2 Dec 03, 2021
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
Optuna is an automatic hyperparameter optimization software framework, particularly designed for machine learning

Optuna is an automatic hyperparameter optimization software framework, particularly designed for machine learning. It features an imperative, define-by-run style user API.

7.4k Jan 04, 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
Fourier-Bayesian estimation of stochastic volatility models

fourier-bayesian-sv-estimation Fourier-Bayesian estimation of stochastic volatility models Code used to run the numerical examples of "Bayesian Approa

15 Jun 20, 2022
A Multipurpose Library for Synthetic Time Series Generation in Python

TimeSynth Multipurpose Library for Synthetic Time Series Please cite as: J. R. Maat, A. Malali, and P. Protopapas, “TimeSynth: A Multipurpose Library

278 Dec 26, 2022
Crypto-trading - ML techiques are used to forecast short term returns in 14 popular cryptocurrencies

Crypto-trading - ML techiques are used to forecast short term returns in 14 popular cryptocurrencies. We have amassed a dataset of millions of rows of high-frequency market data dating back to 2018 w

Panagiotis (Panos) Mavritsakis 4 Sep 22, 2022
This is the code repository for LRM Stochastic watershed model.

LRM-Squannacook Input data for generating stochastic streamflows are observed and simulated timeseries of streamflow. their format needs to be CSV wit

1 Feb 14, 2022
Little Ball of Fur - A graph sampling extension library for NetworKit and NetworkX (CIKM 2020)

Little Ball of Fur is a graph sampling extension library for Python. Please look at the Documentation, relevant Paper, Promo video and External Resour

Benedek Rozemberczki 619 Dec 14, 2022
Empyrial is a Python-based open-source quantitative investment library dedicated to financial institutions and retail investors

By Investors, For Investors. Want to read this in Chinese? Click here Empyrial is a Python-based open-source quantitative investment library dedicated

Santosh 640 Dec 31, 2022
A simple application that calculates the probability distribution of a normal distribution

probability-density-function General info An application that calculates the probability density and cumulative distribution of a normal distribution

1 Oct 25, 2022
Extended Isolation Forest for Anomaly Detection

Table of contents Extended Isolation Forest Summary Motivation Isolation Forest Extension The Code Installation Requirements Use Citation Releases Ext

Sahand Hariri 377 Dec 18, 2022
Project to deploy a machine learning model based on Titanic dataset from Kaggle

kaggle_titanic_deploy Project to deploy a machine learning model based on Titanic dataset from Kaggle In this project we used the Titanic dataset from

Vivian Yamassaki 8 May 23, 2022
A benchmark of data-centric tasks from across the machine learning lifecycle.

A benchmark of data-centric tasks from across the machine learning lifecycle.

61 Dec 28, 2022
This repository contains full machine learning pipeline of the Zillow Houses competition on Kaggle platform.

Zillow-Houses This repository contains full machine learning pipeline of the Zillow Houses competition on Kaggle platform. Pipeline is consists of 10

2 Jan 09, 2022
Python ML pipeline that showcases mltrace functionality.

mltrace tutorial Date: October 2021 This tutorial builds a training and testing pipeline for a toy ML prediction problem: to predict whether a passeng

Log Labs 28 Nov 09, 2022