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I cover this topic quite a lot in my talks - Deep Learning in Java for software engineers. Today you can build pretty much any deep learning model in Java that you can do with Python with availability of plenty of ML & Deep Learning frameworks for Java. If you're interested in this, check out projects like : - https://djl.ai - https://deeplearning4j.org - https://github.com/tensorflow/java - https://github.com/pytorch/java-demo - https://www.deepnetts.com - https://tribuo.org - ONNX support for Java - MLFlow for Java https://docs.databricks.com/applications/mlflow/index.html - SparkMLlib : https://spark.apache.org/mllib/ All of the above listed projects allow one to perform Deep Learning Training and Deployment models into production. Happy to answer any questions on this subject. You can also sign up for my newsletter on this topic https

The tooling is ahead in some ways and behind in others. As a statically typed language, Kotlin's autocompletion and static analysis is already miles ahead. More broadly, the JVM has a rich ecosystem of libraries for data processing, from natural language processing (CoreNLP) to big data analytics (Spark). If you're looking for something similar to Pandas/Numpy/Matplotlib, maybe check out Krangl [1], EJML-Kotlin [3] or lets-plot [4]. There are also efforts to port TensorFlow [5] and PyTorch [6] to the JVM. While not all libraries as mature as their Python cousins, there are a growing number of native libraries and JVM wrappers for scientific computing, which provide most of the functionality and are very ergonomic to use in Kotlin. [1]: https://github.com/holgerbrandl/krangl [2]: https://github.com/mipt-npm/kmath [3]: https://github.com/le