Example projects that demonstrate how to build, train, and deploy ML features and models using the Qwak platform ๐ฆ .
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.