Data Science on the Google Cloud Platform, 2nd Edition

Index

A

• access control, Google BigQuery, Access Control-Access Control
• activation function, Toward More Complex Models
• aggregated streaming data, Streaming Aggregation-Streaming Aggregation
  • (see also time-windowed aggregate features)
• agile architecture, Agile Architecture for Data Science on Google Cloud-Use Managed Services
  • defined, What Is Agile Architecture?
  • managed services, Use Managed Services
  • no-code/low-code decisions, No-Code, Low-Code
• air traffic corridors, Representing Air Traffic Corridors
• Airline On-time Performance Data, Airline On-Time Performance Data-Dataset Fields
• analytics data warehouse, Interacting with the Database
  • (see also BigQuery)
• Analytics Hub, Dataplex and Analytics Hub
• Anscombe's quartet, Exploratory Data Analysis
• Apache Airflow, Cloud Composer
• Apache Beam
  • augmenting dataset with aggregate features using, Apache Beam and Cloud Dataflow
  • batching, Batching Predictions
  • data pipeline construction, Apache Beam/Cloud Dataflow
  • filtering events, Filtering
  • input and output, Input and Output-Input and Output
  • installing into Python environment, Parsing Airports Data
  • invoking model, Invoking Model
  • possible streaming sinks, Possible Streaming Sinks-Querying from Cloud Bigtable
  • Python syntax, Parsing Airports Data
  • reusing endpoint stub, Reusing Endpoint-Per-worker instance
  • reusing transforms, Reuse Transforms
  • starting points, Starting points
  • streaming predictions, Streaming Predictions-Batching Predictions
• Apache Hive, Need for Higher-Level Tools
• Apache Pig, Need for Higher-Level Tools
• Apache Spark ...