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Resource Stacks 📚

Machine Learning projects often vary in their size, from small-scale experimentation to large deployments, meaning that the infrastructure requirements also change and scale. For example, the infrastructure stack needed for deploying an LLM may require a GPU or vector database, which aren't usually needed in more general machine learning use-cases.

Matcha accommodates both of these requirements, and currently offers two infrastructure stacks which we'll discuss in more detail here and show how you can get started with either.

Note: These stacks must be set before provisioning any resources and cannot be change whilst a Matcha deployment exists.

Available stacks


The DEFAULT stack. This stack is ideal for generic machine learning training and deployments and a good starting point. It includes: * Azure Kubernetes Service * ZenML * Seldon Core (deployment) * MLflow (experiment tracking) * Data version control storage bucket

This is the stack used in the getting started page. Follow the link for more information.


The LLM stack: This includes everything found within the DEFAULT stack with the addition of a vector database - Chroma DB. This stack is modified for the training and deployment of Large Language Models (LLMs).

We use this stack for MindGPT, our large language model for mental health question answering.

How to switch your stack

To switch your stack to the 'DEFAULT' stack, run the following command:

$ matcha stack set default

or for the 'LLM' stack:

$ matcha stack set llm

If no stack is set Matcha will use the 'default' stack.

See the API documentation for more information.