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Component guide
Overview of categories of MLOps components.
If you are new to the world of MLOps, it is often daunting to be immediately faced with a sea of tools that seemingly all promise and do the same things. It is useful in this case to try to categorize tools in various groups in order to understand their value in your toolchain in a more precise manner.
ZenML tackles this problem by introducing the concept of Stacks and Stack Components. These stack components represent categories, each of which has a particular function in your MLOps pipeline. ZenML realizes these stack components as base abstractions that standardize the entire workflow for your team. In order to then realize the benefit, one can write a concrete implementation of the abstraction, or use one of the many built-in integrations that implement these abstractions for you.
Here is a full list of all stack components currently supported in ZenML, with a description of the role of that component in the MLOps process:
Type of Stack Component | Description |
Orchestrating the runs of your pipeline | |
Storage for the artifacts created by your pipelines | |
Store for your containers | |
Centralized location for the storage of your secrets | |
Execution of individual steps in specialized runtime environments | |
Services/platforms responsible for online model serving | |
Management of your data/features | |
Tracking your ML experiments | |
Sending alerts through specified channels | |
Labeling and annotating data | |
Data and model validation | |
Builds container images. | |
Manage and interact with ML Models |
Each pipeline run that you execute with ZenML will require a stack and each stack will be required to include at least an orchestrator and an artifact store. Apart from these two, the other components are optional and to be added as your pipeline evolves in MLOps maturity.
Last modified 7d ago