Intro

This post is part of a series of posts with notes as I’m studying for Google’s Professional Data Engineer Certification.

This particular post covers ML topics.

Disclaimer

Please read this disclaimer.

Key Concepts

• L1 vs. L2 regression:
  • L1 estimates the median of the data using absolute value
    • Reduces low-value features
    • Robust to outliers
    • Good when only certain features contribute to success of model
  • L2 estimates the mean of the data to avoid overfitting
    • Not recommended for feature selection
    • Good when all features contribute relatively equally to the success of a model
• TensorFlow
  • Know how TensorFlow can be deployed (also, cost vs. value)
    • CPUs, GPUs, TPUs
• BQ ML
  • Understand the basic flow when using BQ ML:
• Vertex AI
  • Create computation graph and training app
  • Package app
  • Start Vertex AI job to run packaged app