The cognitive bias known as the streetlight effect describes our desire as humans to look for clues where it’s easiest to search, regardless of whether that’s where the answers are. For decades in the software industry, we’ve focused on testing our applications under the reassuring streetlight of GitOps. It made sense in theory: wait for changes to the codebase made by engineers, then trigger a re-test of your code. If your tests pass, you’re good to go.

With automation and CI/CD practices, the entire AI workflow can be run and monitored efficiently, often by a single expert. Still, running AI/ML on GPU instances has its challenges. This tutorial shows you how to meet those challenges using the control and flexibility of CircleCI runners combined with Scaleway, a powerful cloud ecosystem for building, training, and deploying applications at scale.

Amazon Web Services (AWS) provides a vast ecosystem of products that make DevOps an absolute dream. Products like AWS Elastic Beanstalk have ready-made services for autoscaling, deployment, and logging (to name a few). However, teams may prefer to take a barebones approach and build incrementally - in which case AWS Elastic Compute Cloud (EC2) would be the preferred option.

In part 1 of this tutorial, we showed you how to build a large language model (LLM) application that uses retrieval-augmented generation (RAG) to query your own documentation and then test it using a CircleCI continuous integration (CI) pipeline.

Generative AI has already shown its huge potential, but there are many applications that out-of-the-box large language model (LLM) solutions aren’t suitable for. These include enterprise-level applications like summarizing your own internal notes and answering questions about internal data and documents, as well as applications like running queries on your own data to equip the AI with known facts (reducing “hallucinations” and improving outcomes).

Incompatible hardware is a common cause of application failures for distributed teams. Most teams depend on containerization tools like Docker to prevent these failures. But is there any way to automate the deployment of Docker images more efficiently and intuitively? In this article, I will show you how simple it is to do this by combining CircleCI and Microsoft Azure to build a CI/CD pipeline for a Dockerized Spring Boot project.

Generative artificial intelligence (AI) is a form of AI that can create new, original content such as text, code, images, video, and even music. Generative AI-powered tools like GitHub’s Copilot and OpenAI’s ChatGPT have the potential to revolutionize the way you develop software, enabling you to be more efficient and creative. Used in the right way, generative AI can streamline workflows, accelerate development cycles, and unlock the potential for innovation.

Most development teams know that testing the application layer of a system (a.k.a the codebase) is of vital importance. Testing the data layer (the database) is just as important. To perform database testing, you construct queries to assert and validate the database operations, structures, and attributes required by the application connecting to the database.

The benefits of automating deployments for your Java Spring Boot application are undoubtable. Not only is it possible to set up images and run tests or compatibility checks before updating the production environment, but CI/CD providers like CircleCI take a step further by streamlining the entire delivery process from code changes to deployment. Many teams assume that the specifics of their development stack or deployment process will make automation difficult to achieve.

Continuous integration (CI) is the process of integrating changes from multiple contributors to create a single software project. A key component for a smooth CI pipeline is testing. Tests prove that the code does exactly what it says on the tin and that it’s safe to merge the code into the central repository. Tests also anticipate edge cases and ensure that the code handles such cases in a deterministic manner.