Machine learning can give a business a competitive edge over others. Machine learning can be difficult to perform manually because of the manual parameter tuning, feature reduction, and choosing the right ML model, etc.
Automating machine learning for your business provides many benefits. Machine learning can be automated by using several AutoML tools. This article discusses how to automate machine learning for businesses.
What is Automated Machine Learning?
Automated ML is the process of automating tasks that are time-consuming and iterative during the ML model development. It involves building efficient machine learning models and maintaining model quality. Automated machine learning provides capabilities like data collection, data preprocessing, model training and validation, and machine learning model management. AutoML simplifies the building of machine learning models.
How to Automate Machine Learning for your business
A business can use business softwares to enhance its operation. A business can also automate machine learning by using the automatic machine learning (AutoML) tools available. A business can use AutoML tools to build complex machine learning models that can impact business operations. Some of the automatic machine learning tools available to automate ML for your business are discussed below.
It is an AutoML tool that can be used by businesses to automate machine learning. It is a tool based on sci-kit-learn. It provides hyperparameter tuning, one-hot encoding, normalization, and PCA. It performs well on small datasets. Auto Sklearn can also be used by businesses to automate machine learning by providing automatic model selection, ensemble construction, and Bayesian optimization. It can also be used to easily build classification and regression models.
It is an open-source automated ML tool, and a no code library in Python that reduces the time it takes to build machine learning models. Businesses can use Pycaret to automate machine learning for business. Data scientists can expand the efficiency of the ML tests by using Pycaret for building ML models.
It is a powerful automated machine learning tool that is based on the python library. It can be used for data preprocessing, and provides models like deep learning, stacking, lightGBM, etc. for training machine learning models. MLBox also automates machine learning by providing robust feature selection, which helps select the right features for training the ML model. It also provides automatic hyper-parameter optimization, model stacking, and optimization.
It is an open-source and distributed automated machine learning platform that was developed by H20.ai. It has support for both Python and R. It can be used by businesses to automate machine learning because it provides automatic model validation, selection, feature engineering, and deployments. It has in-built hyperparameter tuning and provides support for traditional Ml algorithms such as SVM, decision tree, random forest, etc. It has an easy-to-use interface that automates the training of ML models within a user-specified time limit.
It is an automated ML tool that reduces the time and effort required to build ML models for business. RapidMiner can be used by businesses to automate machine learning by building ML models in just five clicks. There is no need for technical expertise. It just requires data to be uploaded, and the outcome specified, then the ML model will produce high-value insights. Rapidminer can be used to build ML pipelines from data exploration to modeling to production
BigML is an automated machine learning tool. It helps to automate the whole machine learning pipeline and not only model selection. It is very easy to use. To build an automated ML model, first, upload training and validation datasets. It then provides a list of the best possible number models using the least number of features through feature selection, model selection, and feature generation.
It is an automated ML tool that incorporates several regression techniques, from simple (linear regression) to classic regression models. It is used to solve simple binary classification and complex multiclass classification problems with more than a hundred different categories. Datarobot makes it easy to automate the creation of ML models with transparency so that it is easy to trust and understand predictions they make.
It is an automated ML tool that can be used by businesses to automate machine learning. It automates machine learning by analyzing time series and sequence data. Splunk can also integrate with data sources and machine learning tools like TensorFlow and other open-source tools. It also offers other ways to automate machine learning by performing automatic detection of outliers, anomalies and generating predictions.
9. Google cloud AutoML
It is an automated ML tool developed by Google and can be used by businesses to automate machine learning. It has an easy-to-use graphical user interface for building machine learning models. It automates machine learning by using a neural network architecture and transfer learning. It can be used to build high accuracy deep neural networks through the use of pre-trained models. Google has a lot of pre-trained models based on deep neural networks and huge datasets. For example, the google vision model can detect objects because it was trained on an object dataset. It saves time and helps perform transfer learning without even knowing how to create neural networks.
10. Azure AutoML
Azure AutoML is a cloud-based service used to automate machine learning models for regression, classification, and forecasting models. It is an automated ML tool that can be used to automate the building of machine learning models and validation. It provides a graphical user interface and software development kit which helps to automate machine learning. It can also be integrated with programming languages like python, C++, java. Etc. It can be used to train fast and accurate ML models. It also provides automatic hyper-parameter tuning and feature engineering.
Benefits of Automated ML for your Business
Automated machine learning for business provides many benefits. Some of these benefits are discussed below.
- Automated ML saves time: It takes away all the difficulty of manually selecting training parameters and features by providing automatic hyper-parameter tuning and feature selection, it also automatically selects a model and quickly trains an ML model.
- Auto ML also bridges skill gaps. It makes building ML models relatively easy and can be done by non-technical people.
- AutoML improves scalability: Automating Machine learning can be done on a large scale with AutoML tools.
- AutoML also guarantees increased productivity, it provides user interfaces and APIs that can be used to increase productivity.
- AutoML reduces error and produces fast and reliable ML models for businesses.
Businesses are already adopting machine learning to increase efficiency and productivity. Automating machine learning can be done with AutoML tools such as Datarobot, BigML, H20, and every other tool discussed in this article. Automating machine learning will help your scale and develop fast and efficient ML models.