User Guide

• Getting started
  • Setting up
  • Loading omega|ml
  • Loading omega|ml from python
• Storing and retrieving data
  • Native Python objects
  • Pandas DataFrames, Series
• Filtering Data
  • Query filtering
  • Large, Out of Core-sized DataFrames
• Aggregation Framework
  • Standard Groupby aggregation
  • Motivating example
  • Math operations
  • Datetime Operators
  • String Operators
  • Cached operations
  • Complex operations
• Working with Machine Learning Models
  • Storing models
  • Using models to predict
  • Using the compute cluster
• Lambda Modules
  • Writing a Lambda Module
  • Deploying a module
  • Executing a module
• REST API
  • API Reference
  • API Semantics
  • Setting up authorization
  • Working with data
  • Working with models
  • Working with jobs
• Implementing a Custom Backend
• Runtime Clusters
  • Celery Runtime (default)
  • Dask runtime (optional)
• Mini-Batch Streaming
  • Creating a stream
  • Implementing a Producer
  • Implementing a Consumer
  • Implementing a custom WindowEmitter