How I Rebuilt zenml.io in a Week with Claude Code
I rebuilt zenml.io — 2,224 pages, 20 CMS collections — from Webflow to Astro in a week using Claude Code and a multi-model AI workflow. Here's how.
While Label Studio excels as a data annotation tool, ZenML offers a comprehensive solution for end-to-end machine learning workflow orchestration. Discover how ZenML seamlessly integrates with data annotation tools like Label Studio, enabling you to streamline your entire ML pipeline from data preparation to model deployment. With ZenML's intuitive pipeline definition, built-in experiment tracking, model and data versioning, and MLOps integrations, you can efficiently manage your ML workflows and focus on delivering high-quality models. Learn how ZenML complements and extends the capabilities of Label Studio, empowering you to build and deploy ML solutions with ease.
Feature-by-feature comparison
ZenML Label Studio | Data Annotation | Integrates with data annotation tools like Label Studio | Provides a user-friendly interface for data annotation |
| ML Workflow Orchestration | Offers end-to-end ML workflow orchestration | Focuses primarily on data annotation |
| Experiment Tracking | Built-in experiment tracking and comparison | No built-in experiment tracking capabilities |
| Model Versioning | Built-in model versioning and registry | No model versioning features |
| Data Versioning | Built-in data versioning capabilities | Supports versioning of annotated datasets |
| MLOps Integration | Seamless integration with MLOps tools and platforms | Limited MLOps integration capabilities |
| Deployment Automation | Automates model deployment and serving | No deployment automation features |
| Collaborative Workflow | Enables collaboration across teams and roles | Supports collaborative data annotation |
| Customization and Extensibility | Highly customizable and extensible | Provides a flexible and customizable annotation interface |
| Community and Ecosystem | Growing community and ecosystem around ZenML | Active community and extensive integrations |
Code comparison
ZenML from zenml import pipeline, step
from zenml.integrations import labelstudio
@step
def preprocess_data(raw_data):
# Preprocess the raw data
preprocessed_data = ...
return preprocessed_data
@step
def annotate_data(preprocessed_data):
# Integrate with Label Studio for data annotation
annotator = Client.active_stack.annotator
annotated_data = annotator.launch(...)
return annotated_data
@step
def train_model(annotated_data):
# Train the model using annotated data
model = ...
return model
@step
def evaluate_model(model, test_data):
# Evaluate the model performance
metrics = ...
return metrics
@pipeline
def ml_pipeline(raw_data, test_data):
preprocessed_data = preprocess_data(raw_data)
annotated_data = annotate_data(preprocessed_data)
model = train_model(annotated_data)
metrics = evaluate_model(model, test_data)
# Run the pipeline
ml_pipeline(raw_data, test_data)from label_studio import Project
# Create a Label Studio project
project = Project.create(title='My Annotation Project')
# Import data for annotation
project.import_tasks(['file1.jpg', 'file2.jpg', 'file3.jpg'])
# Invite annotators to the project
project.invite_annotators(['[email protected]', '[email protected]'])
# Export annotated data
annotated_data = project.export_tasks()
# Use the annotated data for further processing or model training
...
ZenML provides a comprehensive solution for managing the entire ML workflow, from data annotation to model deployment, while Label Studio focuses primarily on data annotation.
With ZenML's built-in experiment tracking, model versioning, and data versioning capabilities, you can easily monitor and compare model performance, ensure reproducibility, and collaborate effectively across teams.
ZenML offers seamless integration with various MLOps tools and platforms, enabling you to automate and streamline your ML pipeline from experimentation to production.
ZenML is highly customizable and extensible, allowing you to tailor your ML workflow to your specific requirements and integrate with your preferred tools and frameworks.
ZenML has a growing community and ecosystem, providing you with resources, support, and opportunities to collaborate with other ML practitioners.
Expand Your Knowledge
I rebuilt zenml.io — 2,224 pages, 20 CMS collections — from Webflow to Astro in a week using Claude Code and a multi-model AI workflow. Here's how.
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