Machine Learning on Google Cloud

– • Build, train, and deploy a machine learning model without writing a single line of code using Vertex AI AutoML
– • Understand when to use AutoML and Big Query ML
– • Create Vertex AI managed datasets
– • Add features to a Feature Store
– • Describe Analytics Hub, Dataplex, and Data Catalog
– • Describe hyperparameter tuning using Vertex Vizier and how it can be used to improve model performance
– • Create a Vertex AI Workbench User-Managed Notebook, build a custom training job, and then deploy it using a Docker container
– • Describe batch and online predictions and model monitoring
– • Describe how to improve data quality
– • Perform exploratory data analysis
– • Build and train supervised learning models
– • Optimize and evaluate models using loss functions and performance metrics
– • Create repeatable and scalable train, eval, and test datasets
– • Implement ML models using TensorFlow/Keras
– • Describe how to represent and transform features
– • Understand the benefits of using feature engineering
– • Explain Vertex AI Pipelines