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.

Modern AI applications are having a dramatic impact on our industry, but there are still certain hurdles when it comes to bringing ML models to production. The process of building ML models is so complex and time-intensive that many data scientists still struggle to turn concepts into production-ready models. Bridging the gap between MLOps and DevSecOps workflows is key to streamlining this process.

With Gartner estimating that over 90% of newly created business software applications will contain ML models or services by 2027, it is evident that the open source ML revolution is well underway. By adopting the right MLOps processes and leveraging the lessons learned from the DevOps revolution, organizations can navigate the open source and proprietary ML landscape with confidence.

MyJFrog is a central management portal for JFrog Platform users and administrators. It provides a single, centralized view to manage and monitor users, subscriptions, resources, and usage. This gives you the control, visibility, and predictability you need to make informed decisions about your environment. MyJFrog Portal If you have multiple JFrog Cloud subscriptions, MyJFrog lets you access and manage them all in one place. Here are just a few of the benefits of MyJFrog.