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tpot - A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming
Consider TPOT your Data Science Assistant. TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.TPOT will automate the most tedious part of machine learning by intelligently exploring thousands of possible pipelines to find the best one for your data.
machine-learning data-science automl automation scikit-learn hyperparameter-optimization model-selection parameter-tuning automated-machine-learning random-forest gradient-boosting feature-engineering xgboost genetic-programmingTransmogrifAI - TransmogrifAI (pronounced trăns-mŏgˈrə-fī) is an AutoML library for building modular, reusable, strongly typed machine learning workflows on Spark with minimal hand tuning
TransmogrifAI (pronounced trăns-mŏgˈrə-fī) is an AutoML library written in Scala that runs on top of Spark. It was developed with a focus on accelerating machine learning developer productivity through machine learning automation, and an API that enforces compile-time type-safety, modularity, and reuse. Through automation, it achieves accuracies close to hand-tuned models with almost 100x reduction in time. Skip to Quick Start and Documentation.
ml automl transformations estimators dsl pipelines machine-learning salesforce einstein features feature-engineering spark sparkml ai automated-machine-learning transmogrification transmogrify structured-data transformersfeaturetools - automated feature engineering
Featuretools is a python library for automated feature engineering. See the documentation for more information. Below is an example of using Deep Feature Synthesis (DFS) to perform automated feature engineering. In this example, we apply DFS to a multi-table dataset consisting of timestamped customer transactions.
feature-engineering machine-learning data-science automated-machine-learning automl scikit-learn automated-feature-engineeringPocketFlow - An Automatic Model Compression (AutoMC) framework for developing smaller and faster AI applications
PocketFlow is an open-source framework for compressing and accelerating deep learning models with minimal human effort. Deep learning is widely used in various areas, such as computer vision, speech recognition, and natural language translation. However, deep learning models are often computational expensive, which limits further applications on mobile devices with limited computational resources. PocketFlow aims at providing an easy-to-use toolkit for developers to improve the inference efficiency with little or no performance degradation. Developers only needs to specify the desired compression and/or acceleration ratios and then PocketFlow will automatically choose proper hyper-parameters to generate a highly efficient compressed model for deployment.
deep-learning model-compression mobile-app automl computer-visionauto-sklearn - Automated Machine Learning with scikit-learn
auto-sklearn is an automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator.
automl scikit-learn automated-machine-learning hyperparameter-optimization hyperparameter-tuning hyperparameter-search bayesian-optimization metalearning meta-learning smacdarts - Differentiable architecture search for convolutional and recurrent networks
DARTS: Differentiable Architecture Search Hanxiao Liu, Karen Simonyan, Yiming Yang. arXiv:1806.09055. NOTE: PyTorch 0.4 is not supported at this moment and would lead to OOM.
deep-learning automl image-classification language-modeling pytorch convolutional-networks recurrent-networks neural-architecture-searchautoml - Google Brain AutoML
This repository contains a list of AutoML related models and libraries.
automl efficientdet object-detection detection efficientnetmorph-net - Fast & Simple Resource-Constrained Learning of Deep Network Structure
FiGS, is a probabilistic approach to channel regularization that we introduced in Fine-Grained Stochastic Architecture Search. It outperforms our previous regularizers and can be used as either a pruning algorithm or a full fledged Differentiable Architecture Search method. This is the recommended way to apply MorphNet. In the below documentation it is referred to as the LogisticSigmoid regularizer. MorphNet is a method for learning deep network structure during training. The key principle is continuous relaxation of the network-structure learning problem. In short, the MorphNet regularizer pushes the influence of filters down, and once they are small enough, the corresponding output channels are marked for removal from the network.
machine-learning deep-learning tensorflow automl neural-architecture-searchAwesome-System-for-Machine-Learning - A curated list of research in machine learning system
A curated list of research in machine learning system. Link to the code if available is also present. Now we have a team to maintain this project. You are very welcome to pull request by using our template.
infrastructure distributed-systems machine-learning deep-neural-networks system deep-learning optimization paper deep-reinforcement-learning inference automl computer-system edge-computing model-database resouce-managementauto_ml - Automated machine learning for analytics & production
auto_ml is designed for production. Here's an example that includes serializing and loading the trained model, then getting predictions on single dictionaries, roughly the process you'd likely follow to deploy the trained model. All of these projects are ready for production. These projects all have prediction time in the 1 millisecond range for a single prediction, and are able to be serialized to disk and loaded into a new environment after training.
machine-learning data-science automated-machine-learning gradient-boosting scikit-learn machine-learning-pipelines machine-learning-library production-ready automl lightgbm analytics feature-engineering hyperparameter-optimization deep-learning xgboost keras deeplearning tensorflow artificial-intelligenceigel - a delightful machine learning tool that allows you to train, test, and use models without writing code
The goal of the project is to provide machine learning for everyone, both technical and non-technical users. I needed a tool sometimes, which I can use to fast create a machine learning prototype. Whether to build some proof of concept or create a fast draft model to prove a point. I find myself often stuck at writing boilerplate code and/or thinking too much of how to start this.
data-science machine-learning automation neural-network scikit-learn sklearn machine-learning-algorithms artificial-intelligence neural-networks data-analysis machine-learning-library machinelearning preprocessing automl multilayer-perceptron-network scikitlearn-machine-learning multilayer-perceptron automl-api automl-algorithms automl-experimentsautoml-gs - Provide an input CSV and a target field to predict, generate a model + code to run it.
Give an input CSV file and a target field you want to predict to automl-gs, and get a trained high-performing machine learning or deep learning model plus native Python code pipelines allowing you to integrate that model into any prediction workflow. No black box: you can see exactly how the data is processed, how the model is constructed, and you can make tweaks as necessary. automl-gs is an AutoML tool which, unlike Microsoft's NNI, Uber's Ludwig, and TPOT, offers a zero code/model definition interface to getting an optimized model and data transformation pipeline in multiple popular ML/DL frameworks, with minimal Python dependencies (pandas + scikit-learn + your framework of choice). automl-gs is designed for citizen data scientists and engineers without a deep statistical background under the philosophy that you don't need to know any modern data preprocessing and machine learning engineering techniques to create a powerful prediction workflow.
machine-learning tensorflow keras xgboost automlfeaturetools - An open source python library for automated feature engineering
Featuretools is a python library for automated feature engineering. See the documentation for more information.
data-science machine-learning scikit-learn feature-engineering automl automated-machine-learning automated-feature-engineeringMindsDB - In-Database Machine Learning
MindsDB enables you to use ML predictions in your database using SQL. MindsDB automates and abstracts machine learning models through virtual AI Tables. It can easily make predictions over very complex multivariate time-series data with high cardinality.
machine-learning clickhouse postgresql ml snowflake mariadb pytorch artificial-intelligence machine-learning-api hacktoberfest automl explainable-ai explainable-ml singlestoreMerlion - Merlion: A Machine Learning Framework for Time Series Intelligence
Merlion is a Python library for time series intelligence. It provides an end-to-end machine learning framework that includes loading and transforming data, building and training models, post-processing model outputs, and evaluating model performance. It supports various time series learning tasks, including forecasting and anomaly detection for both univariate and multivariate time series. This library aims to provide engineers and researchers a one-stop solution to rapidly develop models for their specific time series needs, and benchmark them across multiple time series datasets. The table below provides a visual overview of how Merlion's key features compare to other libraries for time series anomaly detection and/or forecasting.
benchmarking machine-learning time-series forecasting ensemble-learning automl anomaly-detectionawesome-automl-papers - A curated list of automated machine learning papers, articles, tutorials, slides and projects
Awesome-AutoML-Papers is a curated list of automated machine learning papers, articles, tutorials, slides and projects. Star this repository, and then you can keep abreast of the latest developments of this booming research field. Thanks to all the people who made contributions to this project. Join us and you are welcome to be a contributor. Automated Machine Learning (AutoML) provides methods and processes to make Machine Learning available for non-Machine Learning experts, to improve efficiency of Machine Learning and to accelerate research on Machine Learning.
hyperparameter-optimization automl neural-architecture-search automated-feature-engineeringdevol - Genetic neural architecture search with Keras
DEvol (DeepEvolution) is a basic proof of concept for genetic architecture search in Keras. The current setup is designed for classification problems, though this could be extended to include any other output type as well. See example/demo.ipynb for a simple example.
machine-learning computer-vision deep-learning genetic-algorithm keras automl neural-architecture-searchautokeras - AutoML library for deep learning
AutoKeras: An AutoML system based on Keras. It is developed by DATA Lab at Texas A&M University. The goal of AutoKeras is to make machine learning accessible to everyone. Official website tutorials.
machine-learning deep-learning tensorflow keras automl automated-machine-learning neural-architecture-search autodlAdaNet - Fast and flexible AutoML with learning guarantees
AdaNet is a lightweight TensorFlow-based framework for automatically learning high-quality models with minimal expert intervention. AdaNet builds on recent AutoML efforts to be fast and flexible while providing learning guarantees. Importantly, AdaNet provides a general framework for not only learning a neural network architecture, but also for learning to ensemble to obtain even better models.
machine-learning deep-learning tensorflow gpu ensemble automl learning-theory neural-architecture-search distributed-training tpunni - An open source AutoML toolkit for neural architecture search and hyper-parameter tuning.
NNI (Neural Network Intelligence) is a toolkit to help users run automated machine learning experiments. The tool dispatches and runs trial jobs that generated by tuning algorithms to search the best neural architecture and/or hyper-parameters in different environments (e.g. local machine, remote servers and cloud). This command will start an experiment and a WebUI. The WebUI endpoint will be shown in the output of this command (for example, http://localhost:8080). Open this URL in your browser. You can analyze your experiment through WebUI, or browse trials' tensorboard.
automl deep-learning neural-architecture-search hyperparameter-optimization optimizer
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