Model Deployment: Introduction
Table of Contents

Also referred to as model serving, model scoring, putting the model into production, model operationalization, model integration, model implementation, model productionalization, or predicting, model deployment is the process of integrating a machine learning model into a business process to make practical business decisions based on new data, e.g., identifying clients to make retention offers to or scoring the likelihood of a machine part to fail. Deployment can be as simple as producing an Excel report or complicated as automating an elaborate business process.

Model deployment comprises putting model code into a format compatible with existing business systems and building the required data and monitoring infrastructure. Direct coding into a required format can take weeks, even months, to put a model into production because it often entails re-coding the entire model from the language it was written in (e.g., R, Python, Scala, etc) to the languages the enterprise production system can understand (e.g., C++, Java, etc). However, using automatic machine learning (AutoML) platforms such as RapidMiner, KNIME, DataRobot,, Anaconda Enterprise, and Dataiku, can take a few minutes to put a model into production and monitoring.

Goals #

  • Integrate the model into business workflow
  • Ensure stable model performance over its deployment period

Tasks #

  • Implement trial deployments and experiments to ascertain deployment stability and model performance before switching to new model
  • Deploy model in the way that best satisfies the business needs of the project, e.g., as a file, as a input to external application, as a dashboard, etc
  • Automate as much of the deployment process as possible to ensure reproducibility and reliability
  • Collect output of deployed model and feed it back into the training process
  • Track quality of model predictions, i.e, model drift, substandard throughput, sudden changes in performance, etc
  • Draw insights from model outputs and performance and correct faulty assumptions, conceive new hypothesis and/or inform improvements in model architecture
  • Monitor model for safety and compliance with legal and regulatory requirements, i.e., access control, model lineage, audit trails

Roles #

  • Project Sponsor
  • Project Manager
  • Data Engineer
  • Machine Learning Engineer / Data Scientist
  • Business User
  • Domain Expert
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