Gartner Research

Building a Framework for Managing Effective Machine Learning Workloads

Published: 10 April 2019


Many organizations struggle to take data science projects from prototyping to production. This research provides a framework for data and analytics technical professionals to establish best practices through the build, train, deploy and monitor phases of the machine learning development life cycle.

Included in Full Research

  • Prework: The Building Blocks
    • Establish the Role of a Machine Learning Architect
    • Incorporate a Model Management System
    • Ensure Effective DevOps and DataOps
  • Step 1: Build the ML Model
    • Data Wrangling
    • Feature Engineering
    • Model Algorithm Selection
    • Toolsets and Compute Resource Provisioning
    • Gartner Insights
  • Step 2: Train and Test the Selected Models
    • Define Model Accuracy Metrics
    • Identify Training Data and Optimize Hyperparameters
    • Model Deployment for Training
    • Gartner Insights
  • Step 3: Deploy the Trained Model for Inference
    • Performance-Related Service Levels, Access Control and Rollback Strategy
    • Configure Model Serving
    • Model Deployment in Containers
    • Gartner Insights
  • Step 4: Monitor the Inference Engine for Deviation and Performance
  • Follow-Up
    • Multiple Paths
    • Roles and Responsibilities
  • Teams Working in Silos
  • Working Software Is Not Enough
  • Technology Focus


Sumit Agarwal

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