Welcome to the Workshop Repository! This collection of Jupyter notebooks and scripts is designed to showcase how to develop and deploy a machine learning system, in a few hours. It follows the general pattern of the FTI pipelines (feature,training,inference)
- functions/: Contains utility functions and scripts to enhance functionality.
- Main Notebooks:
1- Feature Pipeline - Foot Traffic Data and Feature Store.ipynb: Feature engineering with foot traffic data from bestTime.2- Training Pipeline - Bar Busyness Model.ipynb: Trains machine learning models.3- Inference Pipeline - Inference and Function Calling.ipynb: Inference and function calling mechanisms.
- busyness_model/: XGboost models created in the training pipeline process.
- Feature Engineering: State-of-the-art usage of the feature store for manipulating data as dataframe.
- Model Training: Easily train, evaluate, and optimize machine learning model.
- Embeddings: A bit of LLMs
- Inference Pipelines: Integrate model inference
- real-time stuff A real-time ML System.
- A free Hopsworks account
- A free BestTime account
To get started, clone the repository and install the necessary dependencies:
pip install -r requirements.txtlangchain-community==0.0.38
langchain-core==0.1.52
xgboost==2.0.3
transformers==4.38.2
protobuf==3.20.0
langchain==0.1.10
streamlit==1.31.1
sentencepiece==0.2.0
gradio==4.21.0
torch==2.3.1
pandas==2.1.4
hopsworks==3.7.0
seaborn==0.13.2Clone the repository :)