Example projects that demonstrate how to build, train, and deploy ML features and models using the Qwak platform 🦅.
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:
- Clone this repository.
- Navigate to the example project you're interested in.
- 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.