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.

If you deploy container-based services in Kubernetes, chances are you’re also using infrastructure-as-code to help automate the provisioning and maintenance of the cloud environments where your applications will run. Up until recently, Terraform was “the name” in infrastructure-as-code.

What it means to be “enterprise grade” has changed. In software development, the size of new artifacts and the pace of development has increased dramatically. Developers are now releasing new components daily, if not multiple times a day. With containerization, and now AI/ML models, new pieces of software can be multiple GBs or larger.

It’s a scenario many developers know all too well: a configuration works flawlessly for one team member but doesn’t work for you. Starting a new job brings with it the excitement of fresh challenges and opportunities. However, it also entails the often painful task of setting up your development environment—a process that can be both time-consuming and complex. This setup phase involves installing dependencies, configuring projects, and troubleshooting issues that might arise.

In today’s digitally-driven world, organizations rely heavily on software applications to streamline services, provide operations, engage customers, and drive innovation through digital transformation. Software has also become the lynchpin for securing an entire business’ services and keeping them up and running. Yet, this omnipresent force comes with its own set of challenges.

At JFrog, we believe in practicing what we preach by “drinking our own champagne.” This means that we not only develop and deliver market-leading products but also utilize our own solutions in our development processes. When it comes to managing development environments, we aim to implement the best-in-class approaches.

The simplest way to manage and organize your Docker images is with a Docker registry. You need reliable, secure, consistent and efficient access to your Docker images that’s shared across your team in a central location, including a place to set up multiple registries that work transparently with the Docker client. There are three different repository types in JFrog Artifactory that you will use regularly for all of your package types, including Docker container images.