1. Data pre-processing: Pre-processing the data and preparing it in a structured format can help in removing irrelevant data, and make the learning process more efficient.
2. Feature engineering: When a feature set is prepared carefully, it can lead to better accuracy of the model.
3. Model selection: Expert knowledge in model selection is a must to ensure that the model is optimized for the task.
4. Regularizing the model: Applying regularization techniques to the model can limit overfitting and improve its generalization capabilities.
5. Parameter tuning: Fine-tuning the hyperparameters of the model can be optimized for better performance by applying techniques like grid search, randomized search, etc.
6. Cloud computation and acceleration: Cloud computing systems provide computation resources that can help expedite and optimize the training process. GPUs and TPUs can be leveraged for faster computation.
7. Transfer learning: Transfer learning can be used to improve performance by taking pre-trained models and fine-tuning them to a specific problem.
8. Ensemble methods: Ensemble methods take multiple models trained on a dataset and combine them to improve the accuracy of predictions.
9. Automated Machine Learning tools: Automated Machine Learning tools can help architects in selecting, designing, and optimizing models without manually exploring various combinations of hyperparameters.
10. Model compression: Model compression techniques like pruning, quantizing, and distillation can help in compressing models and reducing the memory footprint while maintaining the model's accuracy levels.
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