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JFrog

The Software Supply Chain Risks You Need to Know

Code that an organization’s developers create is only the beginning of modern software development. In fact, first-party code is likely to be only a small proportion of an application – sometimes as little as 10% of the application’s artifact ecosystem. An enterprise’s software supply chain is made of many parts, from many sources: open source packages, commercial software, infrastructure-as-code (IaC) files, and more.

JFrog Providers Support the Terraform Community

If you’re reading this blog you’re probably at least somewhat familiar with Hashicorp Terraform and the value it brings to managing the deployment and provisioning of infrastructure resources at scale. We’re big fans and users of it ourselves here at JFrog (see how in our recent webinar!).

How To Put Cloud Nimble to Work to Segment Dev/Test from Production

In every workplace, most work gets done at the most cluttered desks. Yet the business also requires an orderly front office to run efficiently. It’s much the same with your DevOps pipeline environments, as the rough and tumble process of innovating code must ultimately produce cleanly released applications. Continuous integration means that developers perform many builds each day, but few of those builds will advance to production repositories.

Recapping Yalla! DevOps 2022

TL;DR Yalla! DevOps 2022 community event — Learning. Networking. Fun. Driven by the DevOps community. All about the DevOps community. Yalla! DevOps was back again this year with an exciting lineup of content ranging from DevOps, DevSecOps, professional development and more. Local speakers from the DevOps community and industry leaders from around the world took the stage making it one of the best DevOps community events this year. Keep reading for a recap of Yalla! DevOps 2022.

7 Ways to Accelerate Cloud Native Development

Modern enterprises understand the need to move away from developing monolithic applications to ones that make best use of the cloud to enable business acceleration at scale and speed. That means transforming development to more resilient cloud native architectures that can be readily deployed to cloud, multi-cloud, and hybrid environments. What does it mean to be cloud native?

Continuous Training and Deployment for Machine Learning (ML) at the Edge

Running machine learning (ML) inference in Edge devices close to where the data is generated offers several important advantages over running inference remotely in the cloud. These include real-time processing, lower cost, the ability to work without connectivity and with increased privacy.