As organizations continue to embrace CI/CD and DevOps in their quest for shorter, more reliable delivery cycles, the choice of programming languages becomes even more critical. The language used to build your applications can affect everything from developer happiness and productivity to your organization’s performance on the four key software delivery metrics.
While there are an increasing number of off-the-shelf machine learning (ML) solutions that promise to adapt to your specific requirements, organizations that are serious about investing in ML for the long term are building their own workflows tailored exactly to their data and the outcomes they expect. To make full use of this investment, ML models must be kept up to date and working from the freshest available data.
Machine learning (ML) has quickly become integral to many businesses. Its rapid adoption across almost every industry is because it is a force multiplier: new data can be learned from and understood as it arrives, while historical data can be revisited with new tools and practices.
Continuous Integration/Continuous Delivery (CI/CD) has become an essential part of modern software development, allowing teams to deliver high-quality code at a faster pace. Teams can either build or buy their CI/CD system. In this blog post, we will compare both options - exploring the advantages and disadvantages of each - and why CircleCI may be the better choice.
There are many benefits of incorporating CI/CD into your ML pipeline, such as automating the deployment of ML models to production at scale. The focus of this article is to illustrate how to integrate AWS SageMaker model training and deployment into CircleCI CI/CD pipelines. The structure of this project is a monorepo containing multiple models. The monorepo approach has advantages over the polyrepo approach, including simplified dependency versioning and security vulnerability management.
GitLab is a collaborative Git repository that fosters open-source development by offering both free open and private repositories. With its extensive features such as issue tracking and wikis, GitLab empowers teams to collaborate effectively and create exceptional software solutions. GitHub and Bitbucket are similar tools. CircleCI has launched support for Gitlab, so it is helpful to learn to use it. In this tutorial, I’ll show you how to push a project to GitLab.
Amid an AI boom and developing research, machine learning (ML) models such as OpenAI’s ChatGPT and Midjourney’s generative text-to-image model have radically shifted the natural language processing (NLP) and image processing landscape. Due to this new and powerful technology, developing and deploying ML models has quickly become the new frontier for software development.
Building full-stack applications can be challenging, especially when developing the backend and frontend at the same time. In this scenario, frontend teams may have to wait for the backend team to finish building an API before they implement. This is where Mirage.js comes in. In this tutorial, you will explore how to use Mirage.js in frontend applications and mock backend requests for services that have not yet been developed.
A pull request (PR) is (quite literally) a request to pull a change into a project’s code or documentation. It is a popular change management process supported by many VCS providers including GitHub, GitLab, Bitbucket, Codeberg, and others. Typically these come with features to track open pull requests, tools to assist in reviewing the changes, the ability to approve—or reject—PRs, and finally to merge approved PRs.
Dynamic application security testing (DAST) is a critical security measure for modern software delivery pipelines. It involves evaluating the security of web applications by actively testing them in real-time, simulating real-world attacks to identify vulnerabilities. As the cybersecurity threat landscape has evolved, DAST has emerged as a key tool for enforcing application security in continuous integration and continuous delivery (CI/CD) pipelines.
