Streamline Your ML Workflow with ZenML: From Data Annotation to Deployment

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(['user1@example.com', 'user2@example.com'])

# Export annotated data
annotated_data = project.export_tasks()

# Use the annotated data for further processing or model training
...