Hands-On Real-Time Machine Learning Demos

This page showcases how to build and deploy machine learning pipelines that handle real-time data using TurboML’s platform.
Whether you’re exploring incremental learning, analyzing the tradeoffs with retraining, or enriching LLM prompts with live features, these notebooks demonstrate end-to-end workflows to jumpstart your own real-time ML solutions.

Effects of Retraining

  • Shows how Static, Batch, and Online Incremental Learning models perform over time.
  • Includes a Windowed Accuracy Analysis to illustrate performance in dynamic environments.
  • Explores trade-offs between frequent retraining and using incremental techniques.

Why Incremental Stateful Algorithms

  • Compares Online Incremental Learning models with XGBoost in dynamic data scenarios.
  • Highlights how incremental models adapt to data distribution shifts without the overhead of repeated full-batch retraining.

Real-Time Streaming Data

  • Showcases an end-to-end pipeline for anomaly detection on streaming stock data.
  • Learn how to set up continuous model updates with minimal latency.

Real-Time Data with LLMs

  • Demonstrates how to inject fresh data from TurboML’s feature platform into LLM prompts.
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