servable

简明释义

英[/ˈsɜː.və.bəl/]美[/ˈsɜr.və.bəl/]

可服务的（serve 的变体）

英英释义

单词用法

同义词

反义词

例句

1.Obviously, if the WAR contains servable static content, you need to enable file serving.

明显地，如果WAR包含可服务的静态内容，则需要启用文件服务。

2.Only some of that information is Web-servable content.

其中只有一些信息是可服务于Web的内容。

3.Obviously, if the WAR contains servable static content, you need to enable file serving.

明显地，如果WAR包含可服务的静态内容，则需要启用文件服务。

4.WAR files contain servable content.

WAR文件包含可提供的内容。

5.The data was processed and is now servable.

数据已经处理完毕，现在可以服务。

6.The system generates a servable output for the user interface.

系统为用户界面生成了一个可服务的输出。

7.After the training, the AI model became servable through an API.

训练完成后，AI模型通过API变得可提供服务。

8.We need to ensure that all models are servable before deployment.

我们需要确保所有模型在部署之前都是可提供服务的。

9.To improve performance, we need to make sure our services are servable at scale.

为了提高性能，我们需要确保我们的服务在规模上是可服务的。

作文

In the world of technology and software development, the term servable refers to the capability of a model or service to be deployed and made available for use. This concept is crucial in machine learning and artificial intelligence, where models are trained on large datasets and need to be integrated into applications for real-world usage. A servable model is one that has been properly configured and optimized to ensure it can handle incoming requests efficiently and deliver accurate predictions or results. When we think about making a machine learning model servable, several steps come into play. First, the model must be trained and validated to ensure its accuracy and reliability. Once this is achieved, the next step is to create an API (Application Programming Interface) that allows other applications to communicate with the model. This API serves as a bridge between the model and the end-users, enabling them to send data to the model and receive predictions in return.Moreover, it is essential to consider the infrastructure required for hosting a servable model. This includes selecting the right cloud service provider, setting up servers, and ensuring that there is sufficient computational power to handle multiple requests simultaneously. Scalability is also a key aspect; as user demand increases, the servable model should be able to scale up seamlessly to accommodate more traffic without any degradation in performance.Another important factor is monitoring and maintenance. Once a model is servable, it is vital to continuously monitor its performance to identify any potential issues or drifts in accuracy. Regular updates and retraining may be necessary to keep the model relevant and effective. This ongoing process ensures that the servable model remains reliable and continues to meet the needs of its users over time.In addition to technical considerations, there are also ethical implications associated with deploying servable models. Developers must be aware of biases in their training data that could lead to unfair or discriminatory outcomes. Ensuring that a model is both effective and ethically sound is a significant responsibility that comes with the territory of creating servable AI solutions.In conclusion, the concept of servable is fundamental in the field of machine learning and AI. It encapsulates the entire lifecycle of a model from training to deployment, and highlights the importance of infrastructure, monitoring, and ethical considerations. As technology continues to evolve, the ability to create and maintain servable models will play a critical role in how businesses and organizations leverage AI to enhance their operations and deliver value to their customers. Understanding what it means for a model to be servable is essential for anyone looking to work in this exciting and rapidly growing field.

在技术和软件开发的世界中，术语servable指的是模型或服务被部署并可供使用的能力。这个概念在机器学习和人工智能中至关重要，因为这些模型是在大型数据集上训练的，需要被集成到应用程序中以供实际使用。一个servable模型是经过适当配置和优化的，以确保它能够有效地处理传入请求并提供准确的预测或结果。当我们考虑如何使机器学习模型servable时，会涉及几个步骤。首先，必须对模型进行训练和验证，以确保其准确性和可靠性。一旦达到这一目标，下一步是创建一个API（应用程序编程接口），允许其他应用程序与模型进行通信。这个API充当模型和最终用户之间的桥梁，使他们能够向模型发送数据并接收预测结果。此外，还必须考虑托管servable模型所需的基础设施。这包括选择合适的云服务提供商、设置服务器，以及确保有足够的计算能力来同时处理多个请求。可扩展性也是一个关键方面；随着用户需求的增加，servable模型应能够无缝扩展，以适应更多的流量，而不会影响性能。另一个重要因素是监控和维护。一旦模型是servable的，就必须持续监控其性能，以识别任何潜在问题或准确度的漂移。定期更新和重新训练可能是保持模型相关性和有效性的必要措施。这个持续的过程确保了servable模型在时间推移中仍然可靠，并继续满足用户的需求。除了技术考虑之外，部署servable模型还有伦理上的影响。开发者必须意识到他们训练数据中的偏见，这可能导致不公平或歧视性的结果。确保模型既有效又符合伦理标准是创建servable人工智能解决方案时需要承担的重要责任。总之，servable的概念在机器学习和人工智能领域中是基础性的。它涵盖了从训练到部署的整个模型生命周期，并强调了基础设施、监控和伦理考量的重要性。随着技术的不断发展，创造和维护servable模型的能力将在企业和组织如何利用人工智能提升运营和为客户提供价值方面发挥关键作用。理解一个模型成为servable意味着什么，对任何希望在这个令人兴奋且快速发展的领域工作的人来说都是至关重要的。