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AutoML libraries are also known as Automated Machine Learning libraries in the field of machine learning, programming languages, and data science. It is now an emerging domain to build multiple machine learning models efficiently and effectively by leveraging these AutoML libraries. Machine learning projects are now very easy to complete with the top AutoML for ML projects. It is very useful for beginners as well as professionals to use AutoML libraries for machine learning projects at any point of time without any potential error. These libraries are popular for providing tools to automatically discover the best machine learning projects with a predictive modelling task. Let's explore some of the top ten AutoML libraries for machine learning projects in 2022.
Auto-Sklearn is one of the top open-source AutoML libraries for machine learning projects. It is known as an automated machine learning toolkit and popular for providing freedom to users from algorithm section and hyperparameter tuning. This AutoML for ML projects leverages Bayesian optimization and meta-learning with Auto-sklearn 2.0.
Auto-ViML is used for completing machine learning projects out of the huge AutoML libraries. It was designed for developing high-performance interpretable models with fewer variables. It helps to automatically build different machine learning projects with a single line of code. There are attractive features in this AutoML library such as SMOTE, Auto_NLP, data time variables, and feature engineering.
One of the top AutoML libraries for machine learning projects is Auto-Keras— an AutoML system based on Keras. It makes machine learning projects accessible to everyone needed. The search is performed with Keras model through TensorFlow tf.keras API. This AutoML for ML projects offers an effective approach to automatically find top-performing models with predictive modeling tasks.
TransmogrifAI is a well-known AutoML library for machine learning projects and runs on top of Apache Spark while being written in Scala. The aim is to enhance the productivity of machine learning developers in ML projects through machine learning automation and API. It helps in training high-quality machine learning models with minimal hand tuning as well as building modular and strongly typed machine learning workflows efficiently.
MLBox is a popular AutoML library for machine learning projects with different features such as fast reading, distributing data preprocessing or formatting, highly robust feature selection and leak detection, accurate hyper-parameter optimization, and prediction with models interpretation. It is focused on drift identification, entity embedding, and hyperparameter optimization.
H20 AutoML is one of the top AutoML libraries for machine learning projects to automate algorithm selection, feature generation, hyperparameter tuning, and iterative modeling. It helps machine learning projects to train and evaluate ML models efficiently without any error. It offers to reduce the need for machine learning expertise to enhance the performance of projects.
One of the well-known AutoML libraries is HyperoptSklearn— a Hyperopt-based model selection among machine learning algorithms in scikit-learn. It is an open-source library for support the suite of data preparation transformation, classification and regression algorithms. This AutoML library is designed for large-scale optimization for models and optimizing machine learning pipelines such as data preparation, model selection, and many more.
AutoGluon is an easy-to-use and easy-to-extend AutoML library for machine learning projects. It helps in automating stack ensembling, deep learning, as well as real-world applications spanning texts and images. It allows to quickly prototype deep learning and machine learning models with a few lines of codes and leverage automatic hyperparameter tuning.
TPOT is a popular AutoML library for automatically discovering high-quality machine learning models for predictive modeling tasks. It is an open-source library with scikit-learn data preparation and machine learning models. It is a Python AutoML tool to optimize machine learning pipelines with genetic programming. It helps to automate regular and tedious tasks with the best suitable one out of thousands of possible pipelines.
Cloud AutoML library provides high-quality custom machine learning models for machine learning projects with minimal effort and maximum performance. It offers an easy-to-use graphical interface for preparing and storing datasets while accessing all kinds of machine learning tools.
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