MLlib

Primary language is Scala, which may be unfamiliar and challenging for developers accustomed to other languages like Python or Java.

While MLlib provides a wide range of algorithms, it lacks some cutting-edge machine learning models and techniques found in more specialized libraries such as TensorFlow or PyTorch.

MLlib operations can be slower compared to standalone ML frameworks because of the additional overhead introduced by Spark’s distributed computing model and data shuffling across nodes.

Apache Spark is optimized for big data processing, which can make it resource-heavy when used with smaller datasets that could be more efficiently processed by other tools like scikit-learn or pandas.

Integrating MLlib into a project tightly couples it with the entire Spark stack, making it difficult and costly to migrate away from Spark if needed in the future.