Machine Learning Model Deployment

What is Model Deployment?

Deployment is the method by which you integrate a machine learning model into an existing production environment to make practical business decisions based on data. It is one of the last stages in the machine learning life cycle and can be one of the most cumbersome. Often, an organization’s IT systems are incompatible with traditional model-building languages, forcing data scientists and programmers to spend valuable time and brainpower rewriting them.

Why is Model Deployment Important?

In order to start using a model for practical decision-making, it needs to be effectively deployed into production. If you cannot reliably get practical insights from your model, then the impact of the model is severely limited.

Model deployment is one of the most difficult processes of gaining value from machine learning. It requires coordination between data scientists, IT teams, software developers, and business professionals to ensure the model works reliably in the organization’s production environment. This presents a major challenge because there is often a discrepancy between the programming language in which a machine learning model is written and the languages your production system can understand, and re-coding the model can extend the project timeline by weeks or months.

In order to get the most value out of machine learning models, it is important to seamlessly deploy them into production so a business can start using them to make practical decisions.

Machine Learning Model Deployment + DataRobot

DataRobot’s AI platform reduces the effort and timelines required for effective model deployment from weeks or months to mere hours:

  1. REST API. Every machine learning model DataRobot builds can publish a REST API endpoint, making it easy to integrate into modern enterprise applications.
  2. On-demand analysis via GUI. datarrobot的预测功能，一个拖放式预测界面，消除了对外部团队(如软件开发和IT)的依赖，并允许用户在需要时获得预测。
  3. 得分代码导出。 datarrobot的得分代码导出提供了一个简单的、独立的所选模型下载。代码可以作为可执行的.jar文件或Java源代码获得，并且可以部署在Java运行的任何地方。
  4. 独立得分引擎。 datarrobot的独立评分引擎将登台环境和生产环境分开，这样模型就可以在一个稳定、孤立的环境中进行测试和实施。独立引擎有能力运行导入的模型，而不需要接触导出模型的开发服务器。
  5. 火花得分。 Spark Scoring with datarrobot允许企业在机器学习的位置为数据打分，从而消除了在中央服务器上传输和托管数据的需要。这使得企业可以在潜在的庞大数据集上运行使用datarrobot生成的模型，而无需改变数据在Hadoop网络上实例化的存储位置。