Operations | Monitoring | ITSM | DevOps | Cloud

JFrog

Shlomi Ben Haim, CEO & Co-Founder, JFrog - EveryOps Matters

Developers used to code. Now they manage, import, secure, build, package, deliver and monitor… the list of every “Ops” is ever-growing. As if this wasn’t enough, they are now being asked by the C-suite to work with data science and machine learning teams in a gold rush of AI and ML-enabled application delivery. In our annual swampUP kickoff, join JFrog Co-founder and CEO Shlomi Ben Haim - alongside a special guest - as we explore how developers may still start with code and CI, but are increasingly asked to hold the reins of EveryOps; building, securing and delivering alongside the machines and systems they created.

High-Performance AI Unleashed

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.

Accelerate Your Migration to JFrog SaaS with the AWS ISV Workload Migration Program

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.

Manage Ansible Collections with JFrog Artifactory

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.

Expanding Artifactory's Hugging Face Support with Datasets

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.

Doing DevOps Your Way On SaaS Solutions: Connecting JFrog CLI to Your JFrog Workers

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.

JFrog & Qwak: Accelerating Models Into Production - The DevOps Way

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.