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.

The zero trust security model is an approach to network security that enforces strict access controls and authentication at every stage of the software development lifecycle. It treats every user, device, and transaction as a security risk and uses the principle of least privilege to restrict access to sensitive resources and minimize the potential attack surface.

Static application security testing (SAST) involves analyzing source code to identify and address potential security vulnerabilities. Using SAST early in development identifies threats before they can affect a live environment. SAST is particularly important for continuous integration and continuous deployment (CI/CD) pipelines. These pipelines automate the integration of new code changes into the main codebase and deploy applications to production environments.

CircleCI supports GitLab as a version control system (VCS). In this tutorial you will learn how to set up your first CircleCI CI/CD pipeline for a project hosted on GitLab. As GitLab can be used either as a SaaS tool, as well as self-managed on-premise installation, I will cover the steps to connect it with CircleCI for both.

In our article on managing static credentials, we discussed the necessity of secrets — the passwords, tokens, and API keys that connect digital services together — and the importance of keeping them secure so that your infrastructure and data are kept safe from intrusion and misuse. For organizations delivering software at scale, managing credentials across multiple teams and projects can quickly become tedious and error-prone, creating bottlenecks and unnecessary risk.