Adam Mathewz
by on January 7, 2020
Amazon’s DJL is an in-depth learning tool-kit that developers use in the process of developing Machine Learning (ML) and Deep Learning (DL) models in Java natively. It results in the simplification of the use of deep learning frameworks. A tool-kit open-sourced just in time for re Invent 2019, DJL provides a set of high-level APIs to train, test, and run inference. Java developers for hire can develop their models or use pre-trained models that data scientists have developed in Python from their Java code.
DJL remains in compliance with Java's motto: "write once, run anywhere (WORA)." It achieves this compliance by being an engine and deep learning framework-agnostic. Developers can write code once that they can run on any engine. At present, DJL offers an implementation for Apache MXNet, an ML engine that simplifies the development process of deep neural networks. DJL APIs utilize Java Native Access to call the correlating Apache MXNet operations. DJL makes arrangements for infrastructure management, providing automatic CPU/GPU detection based on the hardware configuration to ensure excellent performance. The DJL APIs abridge commonly used functions for developing models, enabling Java developers to leverage existing knowledge to simplify the transition process to ML.

The Machine Learning Lifecycle-
The machine learning lifecycle differs from traditional software development lifecycle. It involves six essential steps:
Step 1- Obtaining the data.
Step 2- Cleaning and preparing the data.
Step 3- Generating the model.
Step 4- Evaluating the model.
Step 5- Deploying the model.
Step 6- Obtaining a prediction/ inference from the model.

.This lifecycle produces a machine learning model that you can consult and can return an answer/speculation.
A model is a mathematical representation of trends and patterns that you find in data. Meaningful data is the base for all ML projects.
In step 1, the model obtains data from a reputable and reliable source. In step 2, it cleans and transforms the data and places it in a format from which a machine can learn. Generally. The process of cleaning and transformation the most time-intensive piece of machine learning lifecycle. DJL simplifies this process for Java developers. It allows Java developers to pre-process images with the help of translators. Translators resize images according to given and expected parameters. They can also convert images from color to gray scale.

Java Developers who are now taking to machine learning, make the mistake of underestimating the time they require to clean and transform data. Hence, translators become an excellent means to kick start the entire process. Step 3 is the training process where a machine learning algorithm makes multiple passes/epochs over the data, studies them, attempting to learn the different kinds of items you want to add. You can store all the trends, patterns, and categories that you can find for that product, in the mode itself. Step 4 is a part of training when you evaluate the model to determine how well it can identify a category of your product. If it finds any mistakes, it uncovers them immediately and automatically corrects the errors in no time. In step 5, you deploy the model into a production environment. Once you have landed the model in production, step 6 helps you allow other systems to consume the model. You can dynamically load models into your code. You can also access it through a REST-based HTTPS endpoint.

Key Points to Note-
1. Java Developers can create, train, and deploy machine learning and deep learning models through Java and their favorite IDE
2. DJL eases the process of using deep learning frameworks and supports Apache MXNet currently
3. The fact that DJL is open-source mutually benefits the tool-kit and its users
4. DJL is engine agnostic, i.e., Java developers can write code once and run it on any engine
5. Java developers must develop and gain an understanding of the ML lifecycle and standard ML terms before they attempt to use DJL

Also Read - Best Java blogs you should follow in 2020.
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