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 Hugging Face excels as a platform for model sharing and collaboration, ZenML offers a comprehensive MLOps framework that complements Hugging Face's capabilities. Discover how ZenML's intuitive pipeline definition, experiment tracking, and model and data versioning features streamline your end-to-end machine learning workflows. Leverage ZenML's seamless integration with Hugging Face for model deployment and artifact materialization, and explore ZenML's deployment option on the Hugging Face Hub as a Space for enhanced collaboration. Learn how ZenML and Hugging Face work together to empower you to build, deploy, and manage state-of-the-art models with ease.
Feature-by-feature comparison
ZenML Hugging Face | ML Workflow Management | Comprehensive MLOps framework for end-to-end workflow management | Primarily focuses on model sharing and collaboration |
| Pipeline Orchestration | Intuitive Python-based pipeline definition and orchestration | No built-in pipeline orchestration capabilities |
| Experiment Tracking | Built-in experiment tracking and comparison | Limited experiment tracking features |
| Model Versioning | Native model versioning and registry | Supports model versioning and artifact management |
| Data Versioning | Built-in data versioning capabilities | No native data versioning support |
| Model Deployment | Seamless deployment with Hugging Face integration | Provides model deployment options and APIs |
| Artifact Materialization | Integrates with Hugging Face for artifact materialization | Supports artifact storage and retrieval |
| Community and Ecosystem | Growing community and ecosystem around ZenML | Extensive community and wide range of pre-trained models |
| Collaboration | Collaborative workflow with Hugging Face Space deployment | Focused on model sharing and collaboration |
| Flexibility and Customization | Highly flexible and customizable MLOps framework | Limited customization options for MLOps workflows |
Code comparison
ZenML from zenml import pipeline, step
from zenml.integrations.huggingface import deploy_to_hub
@step
def preprocess_data(raw_data):
# Preprocess the raw data
preprocessed_data = ...
return preprocessed_data
@step
def train_model(preprocessed_data):
# Train the model using Hugging Face's pre-trained models
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)
model = train_model(preprocessed_data)
metrics = evaluate_model(model, test_data)
deploy_to_hub(model) # Deploy the model to Hugging Face Hub
# Run the pipeline
ml_pipeline(raw_data, test_data)from transformers import AutoModelForSequenceClassification, AutoTokenizer
# Load a pre-trained model from Hugging Face Hub
model_name = "distilbert-base-uncased-finetuned-sst-2-english"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Use the model for inference
text = "This movie was fantastic!"
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
sentiment = outputs.logits.argmax().item()
print("Sentiment:", "Positive" if sentiment == 1 else "Negative")
ZenML provides an end-to-end MLOps framework for managing the entire machine learning workflow, while Hugging Face primarily focuses on model sharing and collaboration.
ZenML offers seamless integration with Hugging Face for model deployment, artifact materialization, and collaboration through the Hugging Face Hub Space deployment option.
With ZenML's intuitive Python-based syntax, you can easily define and orchestrate complex ML pipelines, while Hugging Face lacks built-in pipeline orchestration capabilities.
ZenML provides built-in experiment tracking, model versioning, and data versioning features, ensuring reproducibility and facilitating collaboration throughout the ML workflow.
ZenML offers a highly flexible and customizable MLOps framework, allowing you to tailor your workflow to your specific requirements and integrate with various tools and platforms.
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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