Which ML models can I use?
我可以使用哪些 ML 模型？
SageMaker supports various ML algorithms. You can also create your proprietary ML model and deploy it on SageMaker. For example, cluster analysis would likely be trained on your own data because you want to categorize new records into the same categories that you used for previous records. Alternatively, for predicting real-world sports events, you could use a publicly available model because the training data used would be in the public domain.
We expect that domain- or industry-specific predictions will typically be trained on your own data in SageMaker, while undifferentiated ML needs such as machine translation will use external models.
Amazon SageMaker 支援各種 ML 演算法。您也可建立自己專屬的 ML 模型，並在 Amazon SageMaker 上部署。例如，如果您想將新記錄分類放入先前記錄所用的相同類別，可在您自己的資料上訓練叢集分析。另一方面，用於預測真實世界的運動賽事時，則可使用公開可用的模型，因為所用的訓練資料會出現在公開網域。
我們預期網域或產業特定預測通常是在 SageMaker 中您自己的資料上訓練，無差別的 ML 需求則可能使用外部模型。