For an internal product prototype we have traced OpenAI’s Whisper 3 model from Huggingface and made it usable under JAVA via DJL.

With Machine Learning Operations (MLOps) we ensure that data is efficiently and strategically integrated into business processes through regular and automated training, thus contributing to increased revenue. The challenge is to establish and maintain these automated processes.

Developers who wanted to explore neural networks and deep learning using the JVM, and especially Java, had little choice so far. Those who wanted to focus exclusively on Java could not get around DL4J until now. If it had to be the JVM, but not necessarily Java, the MXNet Scala Frontend was also an option. Finally, if a little Python didn’t scare you, you could try a hybrid solution, combining TensorFlow and Java just like we already explained in previous articles.

So far, we have generally steered clear of the areas of text comprehension and text generation by ML in our practical examples for the basic understanding of AI. For good reason, we have focused primarily on two types of problems: classification of images and prediction of numerical values.

After our first presentation of Amazon’s new Deep Learning Framework for Java, DJL, we now want to introduce the basics of Deep Learning under Java with DJL step by step in a series of beginner posts. This is not about quickly copying code snippets, but about really understanding the framework and the concepts.

On October 1st and 2nd the first ML Summit takes place in Berlin. In 12 workshops in three parallel tracks, experts impart practical knowledge on the topics Applications for Business, Machine Learning Basics & Tools and Specialized Topics.

Christoph will give two talks about Java and Machine Learning at JAX 2018

A TensorFlow Session can be executed in Java in the same way as in Python. This post shows how.

Part 3 in the series about Java / TensorFlow Interoperability, showing how to load a TensorFlow SavedModel in Java

Part 2 in the series about Java / TensorFlow Interoperability, discussing how to save a model so it can be reused in a different environment.