User Guide

• Getting started
  • Setting up your environment
• Working with datasets
  • Storing and retrieving data
  • Filtering Data
  • Large, Out of Core-sized DataFrames
• Working with models
  • Introduction to models
  • Capturing model metrics
  • Adding business logic to models
  • Working with ML frameworks
  • Monitoring model and data drift
• Working with notebooks
  • Working with notebooks
  • Running notebooks on the cloud
  • Parallel Execution
  • Scheduling notebooks
• Working with scripts
  • Using pip-installable modules
  • Using mlflow projects
• Working with streams
  • Introduction
  • Creating a stream
  • Writing to a stream
  • Accessing a stream buffer
  • Consuming streaming data
  • Writing streaming apps
  • Deploying streaming apps
• Deploying models and pipelines
  • What is the runtime?
  • Using the runtime for MLOps
  • The omegam-ml REST API
  • Logging runtime execution
  • Monitoring the runtime
• Command-line interface
  • Using the command-line interface
  • Working with models
  • Working with datasets
  • Working with scripts
  • Working with notebooks
  • Working with the runtime
• Advanced features
  • Working with clusters
  • Customizing omega-ml
• Integration
  • Integrate with Gitlab CI/CD