SynapseML

Why we included this project

Teams already invested in Apache Spark get the most out of SynapseML, since it layers SparkML-compatible estimators and transformers directly on the Spark runtime rather than forcing a separate compute stack. The same API runs on a laptop or an elastically resized cluster, and because the library wraps LightGBM, ONNX scoring, OpenCV, and Microsoft cognitive services, one pipeline can mix tabular modeling with text, vision, and anomaly-detection stages. It also works from Python, R, Scala, Java, and .NET, so a polyglot data team can all contribute in the language they already use. For organizations that have standardized on Spark and want to keep training and inference in one codebase, this is a practical default to evaluate.