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.
This blog discusses eleven machine learning frameworks in 2023.
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.
It allows developers to easily scale their machine learning models faster and easier. It ensures effective ML model monitoring and helps maintain and secure a healthy ML lifecycle.
Eleven Automatic Machine Learning Frameworks in 2022
1. 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. It needs to be imported as a python library before usage.
Features of MLbox
- Provides high robust feature selection which helps right selecting the most optimized features.
- It has accurate hyper-parameter optimization capabilities: Hyperparameter tuning is important for building accurate machine learning models. MLbox ensures that the chosen hyperparameters impact the model effectively.
- It offers mainly three sub-packages related to preprocessing: Fast and distributed data cleaning abilities. Optimization: to build ML models by applying feature engineering and model stacking, and prediction (to predict outcomes on a test dataset)
2. 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.
Features of TPOT
- TPOT only works with clean data and does not perform any preprocessing of the dataset. But it can perform model selection, hyperparameter optimization for building accurate machine learning models.
- It uses classifier methods: This makes it very efficient for regression and classification problems.
- It can analyze pipelines and provide the option of python code.
3. 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.
Features of H20
- It supports traditional ML models and neural networks. Traditional ML models such as linear and logistic regression, support vector machines (SVM), decision trees, random forest, etc.
- It supports both R and Python programming languages, but it requires a java runtime since it was developed in Java.
- It automates model validation, selection, feature engineering, and deployment.
- It uses an exhaustive search for feature engineering and hyper-parameter tuning.
- It has an in-built leaderboard view of the model being trained with its performance.
4. 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.
Features of Google Cloud ML
- It uses neural network architecture and transfers learning. A neural network architecture uses a neural network to design another network. Transfer learning is the process of using a pre-trained model.
- It is not an open-source library, it is a paid subscription, but the cost depends on training models and prediction.
5. TransmogrifAI
It is an automated ML framework that was developed by the Salesforce-based Apache spark framework.
Features of TransmogrifAI
- It automates feature selection, feature validation, model selection
- It can be used to quickly train machine learning models with minimal adjustment
- Helps to construct reusable and modular machine learning workflows.
- It is written in Scala.
6. Auto Sklearn
It is an AutoML framework that is built on top of the sci-kit-learn ML library. It is mostly used for small datasets.
Features of Auto Sklearn
- Model selection and Hyper settings
- It is based on Bayesian optimization
- It uses meta-learning and ensemble construction.
- The package contains fifteen classification algorithms, of which fourteen are used for feature preprocessing. The feature engineering methods include one-hot encoding, and PCA.
- It is most suitable for regression and classification problems.
7. 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.
Features of Auto-Keras
- Neural network for hyperparameter tuning in Keras, which makes it easy to train models. It does this by using a set of algorithms to adjust the model’s hyperparameters.
- It uses a classic scikit-learn API design, this makes it very easy to use.
8. Azure AutoML
It is an AutoML framework that automates machine learning pipelines through custom algorithms that are used to train and configure and validate models.
Features of Azure AutoML
- GUI and SDK capabilities to build ML models
- Faster implementation and accurate models and easy hyper-parameter tuning.
- Feature engineering using deep neural networks.
- Build time series and deep learning models.
9. Databricks AutoML
It is an automated machine learning framework that allows users to quickly generate models and notebooks.
Features of Databricks AutoML
- It uses the MLlib library to automate machine learning.
- It automates preprocessing such as feature extraction, model selection, parameter tuning
- It displays the results and provides a jupyter notebook with the source code.
10. SMAC AutoML
Sequential model-based algorithm configuration (SMAC) is an automatic framework for optimizing algorithms.
Features of SMAC AutoML
- It uses local search and tree search algorithms
- It is very efficient for hyperparameter tuning.
11. AutoGluon
It is an easy-to-use AutoML framework that uses deep learning and ensembling. It was developed by AWS.
Features of AutoGluon
- It has an impressive predictive performance in ML and deep learning models on text, tabular data, and images.
- It can be used with Linux and Mac operating systems.
Conclusion
Automatic ML frameworks are important for automating repetitive tasks and automating the process of building ML models. It helps with hyper parameter 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.
