Annotators
Annotating the data in your workflow.
Last updated
Annotating the data in your workflow.
Last updated
Annotators are a stack component that enables the use of data annotation as part of your ZenML stack and pipelines. You can use the associated CLI command to launch annotation, configure your datasets and get stats on how many labeled tasks you have ready for use.
Data annotation/labeling is a core part of MLOps that is frequently left out of the conversation. ZenML will incrementally start to build features that support an iterative annotation workflow that sees the people doing labeling (and their workflows/behaviors) as integrated parts of their ML process(es).
There are a number of different places in the ML lifecycle where this can happen:
At the start: You might be starting out without any data, or with a ton of data but no clear sense of which parts of it are useful to your particular problem. It’s not uncommon to have a lot of data but to be lacking accurate labels for that data. So you can start and get great value from bootstrapping your model: label some data, train your model, and use your model to suggest labels allowing you to speed up your labeling, iterating on and on in this way. Labeling data early on in the process also helps clarify and condense down your specific rules and standards. For example, you might realize that you need to have specific definitions for certain concepts so that your labeling efforts are consistent across your team.
As new data comes in: New data will likely continue to come in, and you might want to check in with the labeling process at regular intervals to expose yourself to this new data. (You’ll probably also want to have some kind of automation around detecting data or concept drift, but for certain kinds of unstructured data you probably can never completely abandon the instant feedback of actual contact with the raw data.)
Samples generated for inference: Your model will be making predictions on real-world data being passed in. If you store and label this data, you’ll gain a valuable set of data that you can use to compare your labels with what the model was predicting, another possible way to flag drifts of various kinds. This data can then (subject to privacy/user consent) be used in retraining or fine-tuning your model.
Other ad hoc interventions: You will probably have some kind of process to identify bad labels, or to find the kinds of examples that your model finds really difficult to make correct predictions. For these, and for areas where you have clear class imbalances, you might want to do ad hoc annotation to supplement the raw materials your model has to learn from.
ZenML currently offers standard steps that help you tackle the above use cases, but the stack component and abstraction will continue to be developed to make it easier to use.
The annotator is an optional stack component in the ZenML Stack. We designed our abstraction to fit into the larger ML use cases, particularly the training and deployment parts of the lifecycle.
The core parts of the annotation workflow include:
using labels or annotations in your training steps in a seamless way
handling the versioning of annotation data
allow for the conversion of annotation data to and from custom formats
handle annotator-specific tasks, for example, the generation of UI config files that Label Studio requires for the web annotation interface
For production use cases, some more flavors can be found in specific integrations modules. In terms of annotators, ZenML features integrations with the following tools.
| Annotator | Flavor | Integration | Notes |
|---|---|---|---|
|
| Connect ZenML with Argilla | |
|
| Connect ZenML with Label Studio | |
|
| Connect ZenML with Pigeon. Notebook only & for image and text classification tasks. | |
|
| Connect ZenML with Prodigy | |
custom | Extend the annotator abstraction and provide your own implementation |
If you would like to see the available flavors for annotators, you can use the command:
The available implementation of the annotator is built on top of the Label Studio integration, which means that using an annotator currently is no different from what's described on the Label Studio page: How to use it?. (Pigeon is also supported, but has a very limited functionality and only works within Jupyter notebooks.)
The various annotation tools have mostly standardized around the naming of key concepts as part of how they build their tools. Unfortunately, this hasn't been completely unified so ZenML takes an opinion on which names we use for our stack components and integrations. Key differences to note:
Label Studio refers to the grouping of a set of annotations/tasks as a 'Project', whereas most other tools use the term 'Dataset', so ZenML also calls this grouping a 'Dataset'.
The individual meta-unit for 'an annotation + the source data' is referred to in different ways, but at ZenML (and with Label Studio) we refer to them as 'tasks'.
The remaining core concepts ('annotation' and 'prediction', in particular) are broadly used among annotation tools.
