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Companies investing in generative AI find that testing and quality assurance are two of the most critical areas for improvement. Here are four strategies for testing LLMs embedded in generative AI apps.
Learn how to build and deploy a machine-learning data model in a Java-based production environment using Weka, Docker, and REST.
Learn how to optimize JVM and JIT compiler performance for better execution speed, memory usage, and resource utilization in your Java applications—and how to check your results.
Use ForkJoinPool to break down computationally intensive tasks and execute them in parallel for better Java application performance.
If you've ever wished for an easier way to track errors in Python-Django apps, Sentry could be the answer to your dreams. Let's get started with Sentry in Python.
Hosting CI/CD in the cloud can speed up interactions between development pipelines and source code repositories and make life easier for developers. There are a surprising number of options.
Sentry is a full-stack error tracking system for JavaScript applications and more. Let's see how it works.
Pants is a fast, remote-friendly, and ergonomic build tool for multiple languages including Python, Go, Java, and Scala. Let’s take a look.
Even simple and early-stage projects benefit from automated build pipelines. Here’s an easy way to set up CI/CD with Node.js, Jenkins, and Git.
How to pull from Git, build a Docker image, and publish the image to Docker Hub.