JFrog ML Examples

Example projects that demonstrate how to build, train, and deploy ML features and models using the JFrog ML product from JFrog.

Table of Contents

1. Overview
2. Documentation
3. Getting Started
4. Pre-requisites
5. Developer Environment Example
6. JFrog Model Examples

Overview

This repository contains example projects that showcase the capabilities of the JFrog ML for MLOps. Each project is designed to be a standalone example, demonstrating different aspects of machine learning, from data preprocessing to model building and deployment.

Documentation

All documentation for the JFrog ML Platform can be found on the JFrog ML Documentation website.

Getting Started

To get started with these examples:

1. Clone this repository.
2. Navigate to the example project you're interested in.
3. Follow the README and installation instructions within each project folder.

Pre-requisites

To use the JFrog ML Platform for MLOps, you will need:

1. A JFrog Platform account with access to the JFrog ML Platform.
2. A termial or command line interface with Conda and Python installed. JFrog ML supports < 3.10 versions of Python.
3. A Poetry installation for managing Python dependencies.

Development Environment Example

We have provided a sample setup for developers to use JFrog ML more effectively. Developer Environment Example

JFrog Model Examples

Example	Category	Model	Info
Customer Churn Analysis			Predicts Telecom subscriber churn using XGBoost [Conda].
Credit Risk Assesment			Predicts loan default risk using CatBoost algorithm [Poetry]
Sentiment Analysis			Performs binary sentiment analysis using a pre-trained BERT model.
Titanic Survival Prediction			Binary classification model for Titanic survival prediction.[Conda]