Example projects that demonstrate how to build, train, and deploy ML features and models using the Qwak platform 🦅.

1. Overview
2. Getting Started
3. Projects
4. Contributing
5. License

This repository contains example projects that showcase the capabilities of the Qwak platform 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.

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.

Example	Category	Model	Info
Fraud Detection with Feature Store			Fraud Detection model with inference based on Online Features
Sentiment Analysis			Performs binary sentiment analysis using a pre-trained BERT model.
Basic Text Generation			Generates text using a pre-trained BERT model.
Credit Risk Assesment			Predicts loan default risk using CatBoost algorithm [Poetry]
Customer Churn Analysis			Predicts Telecom subscriber churn using XGBoost [Conda].
Code Generation			Autoregressive language models for program synthesis and code generation.
Text Generation			A small T5 model pre-trained for generic text generation tasks.[Conda]
Financial Text Generation			T5 base model trained on Financial QA data for domain specific tasks.[Poetry]
Titanic Survival Prediction			Binary classification model for Titanic survival prediction.[Conda]
Sentiment Classification			DistilBERT-based text classifier for Yelp reviews on Qwak platform.[Conda]
Vector Similarity Search			Vectorizes product descriptions for similarity-based search.

We welcome contributions! Please read our contributing guidelines for more information.

This project is licensed under the MIT License. See the LICENSE file for details.