Classic ML

omega-ml has been built from the ground up to support classic machine learning workflows. This includes data preparation, model training, evaluation and deployment. Deployment scenarios range from batch processing to real-time model serving via REST API or as part of an application.

Contents:

• Tutorial for classic ML
  • Loading datasets for training
  • Training models
  • Evaluating models
  • Deploying models
  • Adding business logic to models
  • Serving models for real-time predictions
  • Tracking and monitoring models
• 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 datasets
  • Storing and retrieving data
  • Filtering Data
  • Large, Out of Core-sized DataFrames