The model training phase is just one part of a much larger AI development pipeline:
- Pre-Processing: Cleaning, structuring, and preparing training data.
- Post-Preprocessing: Feature selection, embedding generation, and final dataset refinement.
- Model Training: The learning phase, where the model improves through cycles of optimization.
- Fine-Tuning & Evaluation: Adjusting for real-world performance using human feedback and task-specific data.
Each step in model training brings AI closer to accuracy, efficiency, and real-world usability. With models growing in complexity, efficient training techniques and compute optimizations are more critical than ever.