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.

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.