Adam Mathewz

Java is so well-known because of its features and the ability to extend its functionality. Being proficient in Java requires more than knowing how to use Java and all its components. Today, a skilled JAVA developer for hire also knows how to make his work more efficient and productive by using tools. Here are the best tools that all Java developers must use in 2020: Java Development Kit: Java Development Kit, commonly known as JDK, is a tool essential for writing Java web applications. It contains within itself several things, such as the Java runtime environment, Java compiler as well the APIs of Java. Some of its best features include auto type interface and support for handling multiple exceptions in a catch block. Gradle: Gradle is the default tool for building Android applications. It is built on the features of Apache Maven and Ant. It helps to automate parts of the application. When Java library plugins are used, Gradle can improve dependency modeling and reduce the compile classpath size. It also has a smart classpath. JAVA Developers can use it to avoid any unnecessary compilation when a library's binary interface remains unchanged. Gradle can be used for multiple project builds as well. JUnit: JUnit is an open-source tool for performing unit tests in Java. It simplifies unit testing and supports both the writing and running of unit tests. Java Developers for hire can code faster and improve their quality. The tool also provides annotations for tests so that developers can identify the test methods quickly. Groovy: Groovy is more of a programming language that complements Java. When used alongside Java, it makes the work faster and increases productivity. With Groovy, developers can write scripts such as build scripts and test scripts. It is object-oriented and dynamic and integrates seamlessly with Java. Its syntax is concise and readable and can be used for advanced customization. It has several features similar to some of the best programming languages today, including Python and Ruby. Using Groovy to extend the functionality of Java can have many benefits. Jira: Jira is one of the best tools for Agile development and can be used with Java. It is primarily used as a troubleshooting and bug management tool. Locating bugs and fixing takes very little time when using this tool. As it applies the principles of Agile development, Jira also lets a JAVA developer keep track of the software development process and its progress. Splunk: Splunk is a monitoring tool for Java web applications. It uses a machine-learning algorithm to index log files and analyses content. It is ideal for use in the Internet of Things or IoT applications as well as enterprise-level projects. It can help to understand the risks involved in business and also the security challenges. It helps to tackle these challenges and avoid losses. Selenium: Selenium is a portable tool that is used for web browser automation as well as the automation of web administration. With this tool, developers can quickly test their web pages. It comes with a playback tool that makes it possible to perform tests even without knowing a test scripting language. Selenium supports many browser vendors, of which it is now a fundamental part. It has two essential components- Selenium WebDriver and Selenium IDE. With Selenium WebBrowser, a developer can automate browser-based regression and also scale and distribute scripts. With Selenium IDE, it is easy to reproduce bugs for testing the web application. Docker: Docker is best for enterprise-level applications. It helps to build web applications and in their management. It also helps to maintain website security. Docker is an essential tool for developing container-based websites and web applications because of its integrated container. JAVA Developers for hire can use it to launch their website from the toolbar in Windows or Mac devices. So these were a few top tools that can be used by JAVA developers in 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.