Integrating Scikit with Other Platforms for Better Analytics Of ML/AI Data

Scikit as we know is one of the most popular open-source machine learning libraries, built on NumPy, SciPy, and Matplotlib. Although it provides a wide range of data sets based on the project, if these can be integrated with a GUI-based platform that can provide the end result in a more consumable form, is always an advantage. Integration with Scikit is not complicated and there are many options available. Below are a few of them.


mvlearn integrates seamlessly with Scikit. In Scikit, a dataset is represented as a 2d array of shapes similarly in mvlearn, datasets Xs are lists of views, which are themselves 2d arrays of shapes. Among many tools within mvlearn, mvlearn.compose.ViewTransformer is a handy tool to apply the same sklearn transformer to each view of the Multiview dataset.

At the end of a multiview machine learning pipeline, it is sometimes needed to transform the multiview dataset into a single view dataset. In sklearn methods can then be used on this single view dataset, which becomes simpler with Merge.

A simple way to transform a multiview dataset in a single view dataset is by stacking each feature.


With Neptune and Sklearn integration you can track classifiers, regressors, and k-means clustering results, specifically, log for test predictions probabilities, classifiers, and regressor visualizations, like confusion matrix, precision-recall chart, and feature importance chart, KMeans cluster labels and clustering visualizations, pickled model, classifier and regressor parameters.

For Neptune, all you need is Python 3.7 or later installed, registration with Neptune to log metadata of your project, NEPTUNE_API_TOKEN set to the environment variable.


Cnvrg allows you to instantly connect scikit to automate your work and accelerate your development. It acts as a pre-built container which integrates with Scikit out-of-the-box. It also provides a container-based ML platform. AI library can yield great results when integrated with the machine learning workspace in Scikit.


This is collaborative data science, data engineering, and data analytics platform that combines the data warehouses and data lakes into a lakehouse architecture. It can import data from any source into the Databricks File System (DBFS). You can visualize the data using Seaborn and matplotlib

You can also leverage parallel hyperparameter sweep to train machine learning models on the dataset. Hyperparameter sweep results can be further explored using MLFlow. Models in MLFlow can also be applied to another dataset using Spark UDF.

Apps4Rent Can Help

Apps4Rent offers virtual desktops hosted on the cloud which are scalable based on your requirement for exploring Scikit and any of the integration platform(s). Since the virtual/remote desktop is on the cloud, it can be accessed from any local PC/laptop, Windows, or Mac, which has internet connectivity. No configuration change is required on your local PC/laptop. Call, chat or email our virtual/remote desktop specialists, available 24/7 for assistance.

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