如何为 Spark MLlib 模型提供服务? [英] How to serve a Spark MLlib model?

问题描述

我正在评估用于基于 ML 的生产应用程序的工具，我们的选择之一是 Spark MLlib，但我对如何在训练后提供模型有一些疑问?

I'm evaluating tools for production ML based applications and one of our options is Spark MLlib , but I have some questions about how to serve a model once its trained?

例如，在 Azure ML 中，一旦经过训练，模型就会作为 Web 服务公开，可以从任何应用程序中使用，这与 Amazon ML 的情况类似.

For example in Azure ML, once trained, the model is exposed as a web service which can be consumed from any application, and it's a similar case with Amazon ML.

您如何在 Apache Spark 中提供/部署 ML 模型?

How do you serve/deploy ML models in Apache Spark ?

推荐答案

一方面，使用 Spark 构建的机器学习模型无法以传统方式在 Azure ML 或 Amazon ML 中提供服务.

From one hand, a machine learning model built with spark can't be served the way you serve in Azure ML or Amazon ML in a traditional manner.

Databricks 声称能够使用它的笔记本部署模型，但我还没有真正尝试过.

Databricks claims to be able to deploy models using it's notebook but I haven't actually tried that yet.

另一方面，您可以通过三种方式使用模型:

On other hand, you can use a model in three ways :

• 在应用程序中进行动态训练，然后应用预测.这可以在 spark 应用程序或笔记本中完成.
• 训练模型并保存它(如果它实现了 MLWriter)，然后加载到应用程序或笔记本中并针对您的数据运行它.
• 使用 Spark 训练模型并使用 jpmml-spark 将其导出为 PMML 格式.PMML 允许不同的统计和数据挖掘工具使用相同的语言.通过这种方式，预测解决方案可以轻松地在工具和应用程序之间移动，而无需自定义编码.例如，从 Spark ML 到 R.

• Training on the fly inside an application then applying prediction. This can be done in a spark application or a notebook.
• Train a model and save it if it implements an MLWriter then load in an application or a notebook and run it against your data.
• Train a model with Spark and export it to PMML format using jpmml-spark. PMML allows for different statistical and data mining tools to speak the same language. In this way, a predictive solution can be easily moved among tools and applications without the need for custom coding. e.g from Spark ML to R.

这是三种可能的方式.

当然，您可以考虑一种架构，在该架构中您可以使用 RESTful 服务在每个示例中使用 spark-jobserver 构建以进行训练和部署，但需要一些开发.这不是开箱即用的解决方案.

Of course, you can think of an architecture in which you have RESTful service behind which you can build using spark-jobserver per example to train and deploy but needs some development. It's not a out-of-the-box solution.

您还可以使用 Oryx 2 等项目来创建完整的 lambda 架构来训练、部署和服务模型.

You might also use projects like Oryx 2 to create your full lambda architecture to train, deploy and serve a model.

不幸的是，对上述每个解决方案的描述都非常广泛，不适合 SO 的范围.

Unfortunately, describing each of the mentioned above solution is quite broad and doesn't fit in the scope of SO.

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