The latest News and Information on Continuous Integration and Development, and related technologies.
In a traditional DevOps implementation, you automate the build, test, release, and deploy process by setting up a CI/CD workflow that runs whenever a change is committed to a code repository. This approach is also useful in MLOps: If you make changes to your machine learning logic in your code, it can trigger your workflow. But what about changes that happen outside of your code repository?
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 the dynamic landscape of software development, the adoption of DevOps has become a cornerstone for organizations aiming to deliver high-quality products at a rapid pace. DevOps, a fusion of development and operations, emphasizes collaboration, communication, and automation throughout the entire software development lifecycle.
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.
Kubernetes, often abbreviated as K8s, has revolutionised the way we manage containerized applications. It provides a robust platform for orchestrating and managing containers at scale. One of the key features that sets Kubernetes apart is its powerful metadata system, which includes labels and annotations. In this blog post, we’ll take a comprehensive look at how labels and annotations work in Kubernetes and how you can leverage them to enhance the management of your applications.
