Ask or search…
K
Links
💡

Core Concepts

MLStacks is built around common concepts that are used to describe infrastructure for machine learning and MLOps. This section will introduce you to these concepts and how they are used in MLStacks.

What's a stack?

A Stack is a collection of stack components, where each component represents the respective configuration regarding a particular function in your MLOps pipeline such as orchestration systems, artifact repositories, and model deployment platforms.
As a shorthand, you can think of a stack as a grouping of these components.

What's a component?

Components are the building-blocks of stacks. MLStacks currently supports the following stack components:
  • artifact_store: An artifact store is a component that can be used to store artifacts. (e.g. S3 buckets on AWS)
  • container_registry: A container registry is a component that can be used to store container images. (e.g. ECR on AWS)
  • experiment_tracker: An experiment tracker is a component that can be used to track experiments, including metrics, parameters, and artifacts. (e.g. MLFlow)
  • orchestrator: An orchestrator is a component that can be used to orchestrate machine learning pipelines. (e.g. Airflow)
  • mlops_platform: An MLOps platform is a component that can be used to deploy, monitor, and manage machine learning models. (e.g. ZenML)
  • model_deployer: A model deployer is a component that can be used to deploy machine learning models. (e.g. Seldon Core)
  • step_operator: A step operator is a component that can be used to execute steps that require custom hardware.

How does MLStacks work?

MLStacks is built around the concept of a stack specification. A stack specification is a YAML file that describes the stack and includes references to component specification files. A component specification is a YAML file that describes a component. (Currently all deployments of components (in various combinations) must be defined within the context of a stack.)
Once you write your stack specification, you can then use MLStacks' CLI to deploy your stack to your preferred cloud (or local K3d) provider. Terraform definitions are stored in your global configuration directory. MLStacks allows you to deploy or connect to a remote state store (e.g. S3, GCS, etc.) so that you can collaborate on your stacks and deployed infrastructure with your colleagues.
Your global configuration directory could be in a number of different places depending on your operating system, but read more about it in the Click docs to see which location applies to your situation. This is where the stack specs and the Terraform definition files are located.