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.

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.