您可以在 Google Cloud 上训练、调整和部署机器学习模型。部署模型时，请注意以下关键事项：
- 确保生产数据与训练和评估数据遵循相同的分布。
- 通过收集更多训练数据定期重新评估。
- 如果数据分布发生变化，请重新训练模型。
第 6 步：部署模型
如未另行说明，那么本页面中的内容已根据知识共享署名 4.0 许可获得了许可，并且代码示例已根据 Apache 2.0 许可获得了许可。有关详情，请参阅 Google 开发者网站政策。Java 是 Oracle 和/或其关联公司的注册商标。
最后更新时间 (UTC)：2025-07-27。
[null,null,["最后更新时间 (UTC)：2025-07-27。"],[[["\u003cp\u003eGoogle Cloud provides a platform for training, tuning, and deploying machine learning models.\u003c/p\u003e\n"],["\u003cp\u003eMaintaining data consistency between training, evaluation, and production is crucial for optimal model performance.\u003c/p\u003e\n"],["\u003cp\u003eContinuous model improvement involves regular data collection, reevaluation, and retraining to adapt to evolving data distributions.\u003c/p\u003e\n"]]],[],null,["You can train, tune, and deploy machine learning models on Google Cloud.\nPlease keep in mind the following key things when deploying your model:\n\n- Make sure your production data [follows the same distribution](https://developers.google.com/machine-learning/guides/rules-of-ml/?utm_source=DevSite&utm_campaign=Text-Class-Guide&utm_medium=referral&utm_content=rules-of-ml&utm_term=distribution#training-serving_skew) as your training and evaluation data.\n- Regularly re-evaluate by collecting more training data.\n- If your data distribution changes, retrain your model."]]