Gartner Research

How to Build Machine Learning and Artificial Intelligence Into Production Applications

Published: 01 October 2019


Building ML- and AI-enabled applications requires that you adopt new patterns and processes to support the probabilistic nature of ML and AI models. Application technical professionals should use this guidance framework to bring ML and AI capabilities to their applications.

Included in Full Research

  • Prework
    • Avoid the Science Project Problem
    • Identify the Model Origin
    • Define Service-Level Agreements
    • Model Governance
    • Guidance
  • Step 1: Design and Implementation
    • Identify the Correct Integration Point and Interface
    • Identify the Correct Deployment Mode for Your Model
    • Define the Frequency of Model Updates
    • Identify Any Specific Hardware Requirements
    • Guidance
  • Step 2: Packaging and Testing
    • Model Portability and Optimization
    • Ongoing Dependency Management
    • Model Serving Frameworks
    • Mobile and IoT Device Model Serving
    • Logging and Telemetry Data
    • Standard Operational Metrics and Monitoring
    • Model-Specific Metrics
    • Guidance
  • Step 3: Deployment
    • Spectrum of Deployment Approaches
    • Kubernetes and ML/AI Workloads
    • Deployment Approaches
  • Step 4: Monitoring and Feedback
    • System Availability
    • System-Level Statistics and Logs
    • Continuous Testing and Validation
    • Model-Specific Operational Metrics
    • Guidance
  • Falling Into the “Science Project” Trap
  • Insufficient Logging, Monitoring and Feedback
  • Failing to Track Model Inference Response Time
  • Related Guidance


Fintan Ryan

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