FLAML

Abstract

1. Introduction

FLAML supports AutoML and Hyperparameter Tuning in Microsoft Fabric Data Science. In addition, we've introduced Python 3.11+ support, along with a range of new estimators, and comprehensive integration with MLflow—thanks to contributions from the Microsoft Fabric product team.

Heads-up: AutoGen has moved to a dedicated GitHub repository. FLAML no longer includes the autogen module—please use AutoGen directly.

FLAML is a lightweight Python library for efficient automation of machine learning and AI operations. It automates workflow based on large language models, machine learning models, etc. and optimizes their performance.

2. Objective

A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.

This project demonstrates how Jupyter Notebook, Machine Learning, Python can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
• pip for dependencies
• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/microsoft/FLAML.git
cd FLAML

1. With three lines of code, you can start using this economical and fast
2. You can restrict the learners and use FLAML as a fast hyperparameter tuning
3. You can also run generic hyperparameter tuning for a custom function.
4. Zero-shot AutoML allows using the existing training API from lightgbm, xgboost etc. while getting the benefit of AutoML in choosing high-performance hyperparameter configurations per task.

pip install flaml

pip install "flaml[automl]"

from flaml import AutoML

automl = AutoML()
automl.fit(X_train, y_train, task="classification")

automl.fit(X_train, y_train, task="classification", estimator_list=["lgbm"])

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Deploy the model as a web app with Streamlit, Flask or FastAPI
• Compare against an additional model and report the metric difference
• Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What dataset does the project use and how was it pre-processed?
2. Which algorithm / model architecture is used and why was it chosen over alternatives?
3. How are training and testing data split, and how is overfitting avoided?
4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by microsoft and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.