Categories of MLOps Tools
Overview of categories of MLOps tools
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 tool chain in a more precise manner.
ZenML tackles this problem by introducing Stack that are composed of Stack Components. These stack component 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 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.
This is a full list of all stack components currently supported in ZenML, with a description of that components role in the MLOps process:
Type of Stack Component
Description
Orchestrating the runs of your pipeline
Storage for the artifacts created by your pipelines
Tracking the execution of your pipelines/steps
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
Alerter
Sending alerts through specified channels
Annotator
Labeling and annotating data
Data and model validation
Each pipeline run that you execute with ZenML will require a stack and each stack will be required to include at least an orchestrator, an artifact store, and a metadata store. Apart from these three, the other components are optional and to be added as your pipeline evolves in MLOps maturity.
In the upcoming sections, you will learn about each stack component, its role in further detail, and how to use them in your own ZenML pipelines.
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