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.

Today marks an important milestone as Codefresh joins forces with Octopus Deploy, a leading player in the Continuous Delivery space. For those less familiar with Octopus, they have been at the forefront of delivering cutting-edge Continuous Delivery for VMs, Windows, and recently stepped into Kubernetes as well.

Sharing what i've learned as Cloudsmith's CEO, and how our customers use cloud-native artifact management to build with confidence.

The customer in question is one of the world’s leading providers of technology and telecommunication services. In this guide, we will share how one of their teams migrated from a traditional CI solution to a powerful Internal Developer Portal using Codefresh and Port.

CI/CD platforms are now an integral part of any software development approach. They help teams to automate critical phases of their workflow. From integrating new code seamlessly to deploying updates swiftly, CI/CD tools not only streamline operations but also promote continuous improvement. As we enter 2024, the world of CI/CD tools is more vibrant and essential than ever.

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.

This article is part of our series “Codefresh in the Wild” which shows how we picked public open-source projects and deployed them to Kubernetes with our own pipelines. We will use several tools such as GitHub, Docker, Codefresh, Argo CD, Kubernetes. This guide chronicles how we integrated all those tools together in order to build an end-to-end Kubernetes deployment workflow.

Add these must-haves to your selection matrix to find an artifact management tool that will transform your DevOps and software delivery.

Bitbucket Cloud is excited to announce the general availability release of our integration with Atlassian’s Forge extensibility platform, marking a significant step forward in our journey to build an infinitely extensible code and CI/CD solution; a concept we've labelled the DevOps Automation Platform. Forge is Atlassian's cloud app development platform, allowing developers to host apps on infrastructure that is provisioned, managed, monitored, and scaled automatically by Atlassian.

Every few months, the CircleCI product organization is going to round up some of the cool stuff we’ve created for you. Read on to learn about the new security, productivity, and speed features you can take advantage of.

Level up supply chain security and package management for your organizations Erlang and Elixir teams with Cloudsmith's Private Hex Repositories.

This article is part of our series “Codefresh in the Wild” which shows how we picked public open-source projects and deployed them to Kubernetes with our own pipelines. This week’s pick is starbase-80, a Kubernetes “homepage” application. We will use various tools such as GitHub, Docker, AWS, Codefresh, Argo CD, Terraform. This article chronicles how we integrated all those tools together in order to build an end-to-end deployment workflow.

This article is part of our series “Codefresh in the Wild” which shows how we picked public open-source projects and deployed them to Kubernetes with our own pipelines. This week’s pick is draw.io an online application for drawing different types of diagrams. We will use several tools such as GitHub, Docker, Helm Codefresh, Argo Rollouts, Argo CD. This guide chronicles how we integrated all those tools together in order to build an end-to-end Kubernetes deployment workflow.

This article is part of our series “Codefresh in the Wild” which shows how we picked public open-source projects and deployed them to Kubernetes with our own pipelines. We will use several tools such as GitHub, Docker, AWS, Codefresh, Argo CD, Kustomize. This guide chronicles how we integrated all those tools together in order to build an end-to-end Kubernetes deployment workflow.

Container orchestration, Kubernetes has emerged as a leading platform for managing and deploying containerized applications. One fundamental concept that plays a crucial role in ensuring optimal performance is the Quality of Service (QoS). In the realm of Kubernetes, this concept is applied at the level of Pods, forming the backbone of resource management within the cluster.

In Agile development, where development is repeated in short periods, the key unit of the development cycle is called an iteration. Iterations, consisting of Design, Development, Testing, and Improvement are usually set for 1 to 4 weeks, and they are characterized by completing a full cycle of system development. After completing one cycle and releasing it, known as Iteration 1, the process is repeated with Iteration 2, Iteration 3, and so on.the.

In the context of utilizing physical servers, the complete resources often remain underutilized, and even in the case of laptops, optimal resource utilization is not achieved. To address this challenge, technological advancements have led to the introduction to containers.

The modern software development lifecycle comprises two key phases: continuous integration (CI) and continuous delivery or deployment (CD). In both stages, automation reduces manual labor, minimizing human errors. That enables DevOps teams to focus on mission-critical work instead of continually fixing mistakes. Automating CI/CD also provides a check and balance system for rolling back errors. Aside from ensuring optimal system performance, this also improves other areas, like security and compliance.

Digital threats are part of the development landscape, so how should you audit your software supply chain security to ensure you protect your pipeline? This blog will take you through the threats that are lurking and the steps you can follow to guard against them.

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.

Faulty deployments and other types of erroneous changes may account for around 70% of all application outages. With the prevalence of CI/CD workflows, engineering teams make changes to their applications, services, and infrastructure all the time, which can make it difficult to trace issues to specific changes.

We are excited to announce that unified user management is now generally available for new Bitbucket Cloud workspaces. Unified user management brings Bitbucket user, group, and product access management to Atlassian Admin. This means that you can manage users across your Atlassian tools in one unified place and connect to external directories via Atlassian Access.

At swampUP 2023, JFrog’s annual user conference, Gerard McMahon, Head of Application Lifecycle Management (ALM) Tools and Platforms at Fidelity Investments, shared Fidelity’s cloud migration story and how it supports the overall company philosophy. He explored the company’s focus on ensuring employee satisfaction while delivering great software and value to customers.

GitOps is a set of best practices that build upon the foundation of Infrastructure As Code (IAC) and expand the approach of using Git as the source of truth for Kubernetes configuration. These best practices are the driving force behind new Kubernetes deployment tools such as Argo CD and Flux as well as the Codefresh enterprise deployment platform. Adopting GitOps in a Kubernetes environment is not a straightforward task when it comes to secret management.

Kubernetes stands out as a powerful tool for managing, scaling, and deploying containerized applications. At the heart of Kubernetes lies its service management capabilities, which play a crucial role in facilitating communication between various components within a cluster. In this guide, we delve into Kubernetes services, exploring their types, functionalities, and best practices.

The beauty of deploying SaaS-based applications is that you don’t have to worry about building the infrastructure, hiring engineers to maintain it, staying on top of upgrades or worry about application security. Indeed, these are some of the main benefits you get by using a SaaS offering. However, the world of software is full of trade-offs, so, what do you lose out on?

Since Codefresh launched our GitOps platform and Enterprise version of Argo CD and Argo Rollouts in November 2020, and extended that platform with Argo Workflows in March 2022 we’ve added numerous features, improved installation, management of GitOps instances, improved security, scalability, and so much more. Now, we’re helping our users scale software delivery across environments.

The power of LLMs to solve real-world problems is undeniable, but unfortunately, in some cases, only theoretical. What’s stopping us from getting the most out of OpenAI’s text completion capabilities in production apps? One common problem is the inability to confidently guard against bad outputs in production the way we’re used to doing with non-AI test suites. Let’s go one step deeper. There is no equivalent of code coverage for an LLM.

Progressive delivery is an advanced deployment method that allows you to gradually shift production traffic to a new version with zero downtime. Argo Rollouts is a Kubernetes controller that enables you to perform progressive deployments such as blue/green and canaries on your Kubernetes cluster. At Codefresh, we love Argo Rollouts and have covered several use cases so far such as smoke tests, metrics, config-maps and even performing deployments for multiple microservices.