Infrastructure as Code

Developing machine learning solutions necessitates the allocation of resources such as databases and compute clusters. Traditionally, setting up these resources was done manually, leading to a higher risk of human error and making it more difficult to redeploy infrastructure quickly.

Infrastructure as Code (IaC) offers a method to create and manage a project's infrastructure resources. With infrastructure defined in files, its setup is automated and version-controlled. This approach minimizes errors and enables environments to be replicated quickly and infrastructure to evolve seamlessly.

Although widely adopted in web and data engineering, IaC is less prevalent in machine learning projects. Applying IaC to define different data storage services, model training environments, and scalable infrastructure for deploying models ensures control over components, costs, and importantly, their scalability and adaptability.

Using IaC effectively requires proficiency with tools like Terraform and adherence to their best practices. Infrastructure as code should be maintained with the same attention to detail and quality as application code.

OUR PERSPECTIVE

We advocate for the use of Infrastructure as Code in machine learning projects. This method offers a more agile, scalable, and efficient way to manage infrastructure, facilitating quicker deployments and enhanced consistency. IaC also improves security and maintenance, which are crucial in ML projects.