Operations | Monitoring | ITSM | DevOps | Cloud

JFrog has long supported standards widely used by developers, including OCI container images. We started with our OCI-compliant Docker registry, then followed up with dedicated JFrog Artifactory OCI repositories. In our continued commitment to developer freedom of choice, we’re excited to take another leap forward. JFrog is now fully conformant to OCI v1.1. Source: OCI Conformance Page JFrog is now fully certified to the OCI v1.1 standard.

Machine learning (ML) models are almost always developed in an offline setting, but they must be deployed into a production environment in order to learn from live data and deliver value. A common complaint among ML teams, however, is that deploying ML models in production is a complicated process. It is such a widespread issue that some experts estimate that as many as 90 percent of ML models never make it into production in the first place.

The AI revolution is transforming enterprises faster than you can say, “sudo apt-get install skynet.” According to McKinsey, 65% of organizations now regularly use generative AI, nearly doubling from last year. However, as developers rush to integrate AI into their products, the shift from AI proof-of-concept to production can feel like trying to assemble flat-box furniture in a hurricane.

In the fast-paced, ever-evolving world of software development, the ability to seamlessly migrate and manage workloads on the cloud is a game changer. At JFrog, we’re committed to empowering organizations to achieve their DevOps, DevSecOps, and MLOps goals with speed, security, and efficiency. Migrating these workloads to the cloud offers numerous advantages, including increased scalability, cost efficiency, and improved agility.

The software development industry reacted with excitement to the news about the partnership between JFrog and GitHub and its potential impact on software development operations as covered in our recent webinar.

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.