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Training Overview

The training component enables you to train LDM models on your datasets with full control over the training process. Train models using the NeoLDM architecture optimized for enterprise data.

What is Training?​

Training in NeoSpace is the process of teaching LDM models to learn patterns from your datasets. The platform provides comprehensive tools for configuring, monitoring, and managing the training process.

Key Characteristics:

  • NeoLDM Architecture: Optimized architecture for enterprise data
  • Distributed Training: Train across multiple GPUs and nodes
  • Real-Time Monitoring: Track training progress in real-time
  • Checkpoint Management: Automatic checkpointing and versioning
  • Flexible Configuration: Customize training parameters

Training Overview

Why Train Models?​

Training is essential for:

  • Learning Patterns: Models learn patterns from your data
  • Customization: Customize models for your specific use cases
  • Performance: Optimize model performance for your data
  • Adaptation: Adapt models to your business needs
  • Innovation: Experiment with different architectures and configurations

Use Cases​

Training is used for:

  • Fraud Detection: Train models to detect fraudulent transactions
  • Credit Scoring: Train models for credit risk assessment
  • Personalization: Train models for personalized recommendations
  • Churn Prediction: Train models to predict customer churn
  • Demand Forecasting: Train models for demand prediction

Next Steps​