FLAML

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About

FLAML is ranked #2 of 35 in predictive analytics software on Inferse. It runs on API, Linux, macOS, Self-hosted, Windows.

Compared on predictive analytics software

Free plan: Yesmicrosoft.github.io

Facts

Purpose: FLAML is a fast library for automated machine learning and tuning.microsoft.github.io · 1 Oct 2026
Resource efficiency: It finds accurate models or configurations for common ML/AI tasks with low computational resources.microsoft.github.io · 1 Oct 2026
LLM adaptation: FLAML automatically adapts large language models to applications to reduce monetary costs.microsoft.github.io · 1 Oct 2026
Auto-tuning: Its tuning approach handles large search spaces, heterogeneous evaluation costs, complex constraints, guidance, and early stopping.microsoft.github.io · 1 Oct 2026
Supported tasks: Task-oriented AutoML supports classification, regression, time-series forecasting, panel forecasting, learning to rank, and sequence classification.microsoft.github.io · 1 Oct 2026
Zero-shot AutoML: The flaml.default package recommends data-dependent hyperparameter defaults without runtime tuning.microsoft.github.io · 1 Oct 2026
Python requirement: The installation documentation requires Python version 3.10 or newer.microsoft.github.io · 1 Oct 2026
Model integrations: Optional installations include OpenAI models, CatBoost, Vowpal Wabbit, Prophet, statsmodels, and Hugging Face Transformers.microsoft.github.io · 1 Oct 2026
Distributed integrations: FLAML provides optional Ray and Spark distributed tuning, plus NNI, BlendSearch, and Azure Synapse options.microsoft.github.io · 1 Oct 2026
.NET support: FLAML has a .NET implementation in the cross-platform ML.NET framework, including Model Builder, the ML.NET CLI, and Microsoft.ML.AutoML.microsoft.github.io · 1 Oct 2026
Spark limitation: GPU training is not supported when Spark is used as the parallel backend.microsoft.github.io · 1 Oct 2026
License: The repository is distributed under the MIT License, permitting users to use, copy, modify, publish, distribute, sublicense, and sell the software subject to its conditions.github.com · 1 Oct 2026
Security reporting: The project directs security vulnerability reports to Microsoft Security Response Center for coordinated disclosure rather than public issues.github.com · 1 Oct 2026
Community support: The project provides community support through Discord, issues, discussions, and contributions.microsoft.github.io · 1 Oct 2026
Purpose: FLAML automates machine learning model selection and hyperparameter tuning with low computational resources.microsoft.github.io · 2 Oct 2026
Tuning: Its tuning tool handles large search spaces with varied evaluation costs, constraints, guidance, and early stopping.microsoft.github.io · 2 Oct 2026
Tasks: The documentation lists classification, regression, forecasting, and ranking among the supported AutoML tasks.microsoft.github.io · 2 Oct 2026
Customization: Users can restrict the learners FLAML tries or tune a customized learner.github.com · 2 Oct 2026
Parallel tuning: FLAML supports Ray and Spark backends for parallel tuning, but a tuning job cannot use both.microsoft.github.io · 2 Oct 2026
Integrations: The documentation describes MLflow logging and integration with Azure Machine Learning.microsoft.github.io · 2 Oct 2026
Microsoft Fabric: The repository says FLAML supports AutoML and hyperparameter tuning in Microsoft Fabric Data Science.github.com · 2 Oct 2026
Installation: The Python library can be installed with pip or conda, and optional packages enable features such as AutoML, Hugging Face Transformers, Ray, and Spark.microsoft.github.io · 2 Oct 2026
Runtime requirement: The repository specifies Python 3.10 or later and earlier than 3.14 for its latest version.github.com · 2 Oct 2026
Availability: The repository identifies FLAML as MIT-licensed open-source software.github.com · 2 Oct 2026
Support: The official site links to a Discord community.microsoft.github.io · 2 Oct 2026