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Fast, lightweight AutoML and hyperparameter tuning for Python
FLAML delivers rapid, cost-effective AutoML and hyperparameter optimization for classification, regression, and LLM workflows, supporting Python 3.9+, MLflow integration, and a multi-agent AutoGen framework.
When teams consider FLAML, these hosted platforms usually appear on the same shortlist.
Cloud service for accelerating and managing the machine learning project lifecycle, including training and deployment of models
Automated machine learning platform for building AI models without coding
Unified ML platform for training, tuning, and deploying models
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Rapid tabular classification baseline
Generate a high‑accuracy classifier in minutes, reducing model development time
LLM inference cost optimization
Tune ChatGPT/GPT‑4 generation parameters to meet budget constraints while preserving response quality
Multi‑agent financial analysis chatbot
Deploy AutoGen agents that retrieve market data, compute YTD gains, and present results through natural language dialogue
MLOps pipeline with MLflow tracking
Automatically log experiments, models, and hyperparameter configurations to MLflow for reproducible deployments
FLAML requires Python 3.9 or newer and includes support for Python 3.11.
It focuses on speed and low computational cost, using cost‑aware search and a scikit‑learn‑style API.
Yes, FLAML integrates natively with MLflow for experiment tracking and model registry.
AutoGen is currently in preview (v2.0.0); it is functional but may evolve.
Install with pip using `pip install "flaml[autogen]"` to pull required dependencies.
Project at a glance
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