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19 January, 2021

Top 5 Low-Code/No-Code ML libraries for Data Scientists

Low-Code and No-Code platforms are going to be game-changer for tech professionals across the world. The increasing number of low-code and no-code machine learning (ML) libraries is making it extremely faster and easier to develop top-notch projects. Here are top no-code and low-code libraries that you should be aware of.

1. Pycaret

  • This is an open source, low-code machine learning (ML) library in Python which automates ML workflows.
  • It is an end-to-end machine learning and model management tool that speeds up the experiment cycle exponentially and makes you more productive.
  • You can easily tune the hyperparameters of the various models on GPU.

2. H2O AutoML

  • Automated machine learning (AutoML) is the process of automating the end-to-end process of applying machine learning to real-world problems. AutoML tends to automate the maximum number of steps in an ML pipeline — with minimum amount of human effort — without compromising the model’s performance.
  • H2O AutoML is an automation tool used as the combined interface for multiple models and algorithms.
  • It is fully open-source, distributed in-memory machine learning platform with linear scalability.
  • It supports both Python and R programming languages. For beginners, it helps to automate preprocessing, training, validation and fine-tuning models.

3. Auto-ViML

  • This low-code library is also known as 'Auto_ViML' or “Automatic Variant Interpretable Machine Learning” (pronounced “Auto_Vimal”). It accepts any dataset that is in the form of the Pandas data frame.
  • One of the library’s unique differentiators is that it performs feature reduction (or feature selection) automatically in order to produce the simplest model which in this case is the model with the least number of features needed to produce reasonably high performance.
  • This tool performs category feature transformation and simple data cleaning steps such as identifying missing values as “missing” so that they can be best left to the model to decide how to use them.
  • Auto-ViML provides verbose output to allow for a great deal of understanding and interpretability.

4. Create ML

  • Create ML 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.
  • You can also train models to perform tasks like recognizing images, extracting meaning from text, or finding relationships between numerical values.
  • Before the training, you can set the iteration count and fine-tune the metrics. For models such as style transfer, Create ML 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.
  • Rather than starting from scratch when training models from your data, Google Cloud AutoML implements automatic deep transfer learning and neural architecture search for language pair translation, natural language classification, and image classification.
  • Google Cloud AutoML helps developers with limited ML expertise to build models specific to their use-case and business needs.

Source: https://content.techgig.com/no-code-ml-libraries-for-data-scientists/articleshow/79648166.cms