From Notebook to Production: Bridging the Gap Between Data Science and ML Engineering

Google Cloud Build with AI AI Infrastructure
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Hey everyone! Let’s talk about a challenge many teams face: the “notebook to production” gap.

That beautiful Jupyter notebook with 99% accuracy often fails when it hits real-world data and traffic. Here’s how we bridge this gap using Google Cloud tools.

The Problem Flow:

Data Science Experiment → "It works on my machine!" → Production Disaster :scream:

The Google Cloud Solution Flow:

Phase 1: Collaborative Development (Data Science)

  • Vertex AI Workbench: Develop and experiment in managed notebooks

  • BigQuery ML: Prototype models directly on your data with SQL

  • Experiment Tracking: Use Vertex AI Experiments to log every run, parameter, and metric

Phase 2: Reproducible Pipelines (ML Engineering)

python

# Vertex AI Pipelines - From experimental to production-ready
@component
def preprocess_data(...):
    # Same logic as notebook, but containerized
    return processed_data

@component  
def train_model(...):
    # Versioned training code
    return model

@pipeline
def my_ml_pipeline():
    preprocess_task = preprocess_data()
    train_task = train_model(preprocess_task.output)

Phase 3: MLOps & Monitoring

  • Vertex AI Model Registry: Version control your models

  • Vertex AI Endpoints: Auto-scaling deployment

  • Vertex AI Monitoring: Detect training-serving skew, data drift, and performance degradation

Key Mindset Shifts:

  • Data Scientists: Think about data contracts and model signatures early

  • ML Engineers: Understand the business problem and model behavior

  • Both: Embrace experimentation, but design for production from day one