Best automatic machine learning frameworks to consider in 2022

Best automatic machine learning frameworks to consider in 2022. Let us run by a brief concept of Machine Learning and some of the most used Machine Learning frameworks and in trend in 2022. Top tech trends in Nigeria, e-learning, Fintech, and remote working.

Digitalization is the bedrock of information technology and science. Businesses around the world are increasingly investing in digitalization at a frantic pace with Machine Learning (ML) and Artificial Intelligence (AI) witnessing significant adoption in day to day operations of organizations.

Machine learning is used in almost every sector, mostly in every industry such as Agriculture, finance, healthcare, and marketing. AutoML frameworks are a very important part of machine learning.

An automatic machine learning framework can help a business scale its operations and maintain an efficient ML lifecycle. It also allows anyone to build machine learning models efficiently. Machine learning engineers and data scientists can accelerate ML development using AutoML frameworks.

What is an Automatic Machine Learning Framework? An automatic machine learning framework is an interface that allows developers, machine learning engineers, and data scientists to build and deploy their machine learning models efficiently.

Auto-Keras

It is an open-source automated machine learning framework that uses neural architecture search algorithms to automate the machine learning process. It was developed by the DATA lab and is built on top of Keras.

MLBox

It is an automated machine learning framework that is mainly used in data preparation, model selection, and hyper-parameter search. It is a python based ML library and needs to be imported as a python library before usage.

TPOT

TPOT is an open-source machine learning framework built on top of Scikit-learn that uses regression and classification algorithms. It uses genetic algorithms for model optimization, which allows it to explore thousands of possible pipelines to discover the best model pipeline for a given dataset.

H20

It is an open-source AutoML framework that has a distributed memory and was developed by H20.ai. It can be used to perform many tasks that require many lines of code at the same time.

Google Cloud ML

It is a google based AutoML framework with a graphical user interface that is simple to use for building machine learning models.

Conclusion – automatic machine learning frameworks

Automatic ML frameworks are important for automating repetitive tasks and automating the process of building ML models. It helps with hyperparameter tuning, feature engineering, model selection, and many other features. This blog post discussed 11 automatic machine frameworks that can be used by business and ML engineers for automation and to quickly build accurate ML models.

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