SeqLab
2024
Machine Learning Experiment Automation Framework
Developer
This project provides a comprehensive framework for training and evaluating various machine learning models, focusing on multi-feature sequential categorical data.
SeqLab is engineered to facilitate systematic experimentation and benchmarking of machine learning models (Markov, LSTM, Transformer, GPT). Utilizing a configuration-driven approach, researchers and practitioners can specify their experimental setups through a JSON file, ensuring reproducibility and flexibility.
The project integrates seamlessly with MLflow, providing robust tools for experiment tracking and model management.