Definition
Scaling is the active methodology of expanding AI systems by adding more layers, neurons, or training examples. It includes techniques like distributed training across multiple GPUs to handle increased loads. Effective scaling requires balancing model complexity with available hardware to avoid diminishing returns or overfitting, ensuring that the increase in size translates directly to improved predictive accuracy and robustness.
Summary
Scaling is the process of adjusting model size or data volume to enhance learning capabilities and performance.
Key Concepts
- Distributed Training
- Model Complexity
- Overfitting
- Hardware Acceleration
Use Cases
- Distributed deep learning
- Increasing neural network depth
- Expanding training corpora