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 in order to start using it 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 make sure 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 deploy them into production as seamlessly as possible so the business can start using them to make practical decisions.

Machine Learning Model Deployment + DataRobot

DataRobot’s automated machine learning platform reduces the effort and timelines usually required for effective model deployment from weeks or months down to 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. DataRobot’s Predict functionality, a drag-and-drop prediction interface, removes the dependency on external teams like software development and IT and allows users to get predictions when they need them.
  3. Scoring code export. DataRobot’s Scoring Code Export offers a simple, self-contained download of the chosen model. The code is available as an executable .jar file or as Java source code, and can be deployed anywhere Java runs.
  4. Standalone scoring engine. DataRobot’s Standalone Scoring Engine separates these two environments so that models can be tested and implemented in a stable, isolated environment. The Standalone Engine has the capability to run imported models without ever touching the development server from which they were exported.
  5. Spark scoring. Spark Scoring with DataRobot allows enterprises to score data for machine learning where it is located instead of having to transfer and host that data on a central server. This allows businesses to run models produced using DataRobot on potentially huge datasets without changing the storage location of the data from its instantiation on a Hadoop network.