If you work with virtual machines or install and configure software on EC2 or leverage dynamic runtimes, chances are you’re also using Ansible. In fact, JFrog has supported installation via Ansible for some time. If they’re not using Red Hat, the way most organizations have managed their Ansible Collections – including Roles – is by storing them in Git repositories.

When working with ML models, it’s fair to say that a model is only as good as the data it was trained on. Training and testing models on quality datasets of an appropriate size is essential for model performance. Because of the intricate link between a model and the data it was trained on, it’s also important to be able to store datasets and versioned models together.

In our previous blog post, we explored JFrog Workers, a JFrog Cloud Platform service that allows you to create customized workers that can respond to events in the platform. These workers can perform various tasks, from running code to adjusting functions, giving you more flexibility and control over your workflows. Allowing you to automate processes and streamline your development pipeline in a serverless execution environment.

As we delve deeper into the era of software reliance, the 2024 JFrog Software Supply Chain report emerges as required reading for developers and DevOps professionals who are at the frontline of today’s technological innovations.

We are collectively thrilled to share some exciting news: Qwak will be joining the JFrog family! Nearly four years ago, Qwak was founded with the vision to empower Machine Learning (ML) engineers to drive real impact with their ML-based products and achieve meaningful business results. Our mission has always been to accelerate, scale, and secure the delivery of ML applications.

Generative AI and Large Language Models (LLMs) are the new revolution of Artificial Intelligence, bringing the world capabilities that we could only dream about less than two years ago. Unlike previous milestones, such as Deep Learning, in the current AI revolution, everything is happening faster than ever before. Many feel that the train is about to leave the station, and since we are talking about bullet trains – every day matters.

The latest JFrog collaboration with GitHub enables you to easily combine your favorite solutions for source code and binaries in a seamless integration. This means you now have a unified comprehensive and secure end-to-end experience that supports your software projects. This integration covers everything from curating open source packages, coding, CI, release management, deployment and production. Including three major improvements to your developer experience.

Just a few years back, developing AI/ML (Machine Learning) models was a secluded endeavor, primarily undertaken by small teams of developers and data scientists away from public scrutiny. However, with the surge in GenAI/LLMs, open-source models, and ML development tools, there’s been a significant democratization of model creation, with more developers and organizations engaging in ML model development than ever before.