Low-Code and No-Code platforms are going to be game-changer for tech professionals across the world. The increasing number of low-code no code ML libraries is making it faster and easier to develop projects. Here are top low code libraries that you should be aware of.
This is an open source, low-code machine learning (ML) library in Python. The library automates ML workflows. It is an end-to-end ML and model management tool. You can easily tune the hyperparameters of the various models on GPU. Update the deploy_model function and even plot the model function included with scale parameter.
2. H2O AutoML
This is another automation tool used as the combined interface for multiple models and algorithms. It supports both Python and R programming languages. For beginners, it helps to automate preprocessing, training, validation and fine-tuning models.
This is another low-code library known as ‘Auto_ViML’. It accepts any dataset that is in the form of the Pandas data frame. The tool can perform data cleaning and category feature transformation. Auto-ViML provides verbose output to allow for a great deal of understanding and interpretability.
This is a purely no-code drag and drop solution developed by Apple. It works on macOS and comes with a bunch of pre-trained model templates. Before the training, you can set the iteration count and fine-tune the metrics. For models such as style transfer, CreateML provides real-time results on the validation model.
5. Google Cloud AutoML
Google has created the Apple-like AutoML tool. AutoML by Google Cloud offers various natural language, AutoML translation, and video intelligence products. It also helps developers with less ML expertise to build models specific to their use-case.
Sources : https://content.techgig.com/no-code-ml-libraries-for-data-scientists/articleshow/79648166.cms