mlxtend adds helper modules for Python data analysis and machine learning libraries.
Microsoft ML for Apache Spark vs astroML
How do Microsoft ML for Apache Spark and astroML compare? Here are the facts side by side: pricing, platforms, open-source status and what each one is for.
| Directory detail | Microsoft ML for Apache Spark | astroML |
|---|---|---|
| What it is | SynapseML provides distributed machine learning algorithms for Spark through its Python library. | AstroML provides Python tools for machine learning and data mining in astronomy. |
| Website | ||
| Type | Open-source project | Open-source project |
| Topic | ||
| Pricing | Not confirmed | Not confirmed |
| Platforms | Not recorded | Web |
| Open source | Yes | Not stated |
| Language | English | — |
| Alternatives |
Other options to consider
Sites similar to both Microsoft ML for Apache Spark and astroML.
Machine learning pipelines can use whylogs, an open-source library for logging data quality and model performance.
For Python data preparation, DataPrep offers an open-source low-code library to collect, clean, and visualize data.
Vaex provides out-of-core DataFrames for Python to explore and visualize large tabular datasets, with support for machine learning workflows.
Class-imbalanced machine learning is supported by imbalanced-ensemble, a toolbox for ensemble methods and datasets.
Sparkling Water brings H2O machine learning functionality into an Apache Spark cluster.






