Model Operationalization

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This course digs deeper into model operationalization, and the specifics for how to put models for inference into various places of deployment and operations. We’ll go over the different ways you operationalize your model including on the edge, in the cloud, on premise, and hybrid options. This intermediate course is designed for those implementing AI.

What Will I Learn?

  • What is Model “Operationalization”?
  • Training Phase vs. Inference Phase
  • The Four Different AI Tech Environments
  • The Operational Environment
  • Model Operationalization Approaches
  • Operationalization at the Edge
  • Operationalization on Premise
  • Operationalization in the Cloud
  • ML Model Microservices
  • ML “Ops”
  • Model Lifecycle Management

Who is this Training For?

  • Project Manager
  • Product Manager
  • Capability Manager
  • Technical Manager
  • Acquisitions Manager
  • Procurement & Contracting Officers
  • Computer Systems Programmer
  • Developer
  • AI Research Associate
  • Data Scientist
  • AI / Machine Learning (ML) EngineerAI Assurance Engineer
  • Test & Evaluation Engineer, System Engineer
  • Network Analyst
  • Data Analyst
  • Operations Research Analyst
  • Deployment Engineer
  • Knowledge Operations Manager
  • Network Infrastructure Engineer
  • Information Technician
  • Data engineer
  • Network operations
  • Information technician
  • Data technician
  • Program manager
  • Project manager
  • Supply program manager
  • Designers

Supported Learning Paths

  • Cognilytica: CPMAI Certification
  • DoD JAIC: Drive AI, Create AI, Embed AI, Facilitate AI
  • Edison DSF: Data Science Professionals (DSP04 -DSP09), Data Engineering & Management Professionals (DSP10-DSP16)

Learning Levels

  • DoD JAIC AI: Intermediate (Applied) Level
  • Edison DSF: Level 4-5
  • Category: Implementing AI