Model training forms the core of any machine learning (ML) project, and having a trained ML model is essential to adding intelligence to a modern application. A performant model is the output of a rigorous and diligent data science methodology. Not implementing a proper model training process can lead to high infrastructure and personnel costs because it underlines the experimental phase of the ML process and by nature tends to be highly iterative.
Generally speaking, training a model from scratch is time-consuming and compute intensive. When the training data is small, we can’t expect to train a very performant model. A better alternative is to fine-tune a pretrained model on the target dataset. For certain use cases, Amazon SageMaker provides high-quality pretrained models that were trained on very large datasets. Fine-tuning these models takes a fraction of the training time compared to training a model from scratch.
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