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Machine Learning Integration

Overview

This tutorial will teach you how to integrate machine learning capabilities into your Spezi application. You'll learn to build health data analysis, predictive modeling, and anomaly detection features.

What you'll build: An intelligent health application with ML-powered insights, predictions, and automated health monitoring.

What you'll learn: - Core ML integration with Spezi - Health data preprocessing and feature engineering - Predictive modeling for health outcomes - Anomaly detection and alerting - Model deployment and management

Prerequisites

  • Completion of the Template Application Setup tutorial
  • Basic understanding of machine learning concepts
  • Familiarity with Core ML and Create ML
  • Knowledge of data science and statistics

Coming Soon

This tutorial is currently under development. It will include:

  • Complete ML integration guide
  • Health data analysis workflows
  • Predictive model development
  • Real-time ML processing
  • Model optimization and deployment

What to Expect

The tutorial will cover:

  1. ML Framework Setup - Configuring Spezi for machine learning
  2. Data Preprocessing - Health data cleaning and feature engineering
  3. Model Development - Building health prediction models
  4. Anomaly Detection - Identifying unusual health patterns
  5. Real-time Processing - Live ML inference and predictions
  6. Model Management - Versioning and updating ML models
  7. Performance Optimization - Optimizing ML performance
  8. Privacy-Preserving ML - Federated learning and differential privacy

Coming soon! This tutorial will provide comprehensive ML integration guidance for Spezi applications. Explore the Machine Learning documentation for current ML capabilities.