Best Practices & Methodologies for Successful AI Implementation

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Knowing best practices for implementing AI on a large scale is critical for the success of your project. Existing methodologies that are either application development-centric or enterprise architecture focused or rooted in hardware or software development approaches face significant challenges when faced with the unique lifecycle requirements of AI projects. It’s also important to know AI’s impact on your strategy as well. In this course we’ll walk through different Methodologies including CRISP-DM and CPMAI to provide you with the foundation needed to project success. This course is geared towards executives or managers running AI projects.

What Will I Learn?

  • The Data Foundation to AI
  • Data-centric Methodologies
  • Overview of CRISP-DM Methodology
  • Adapting CRISP-DM for Agile and AI
  • Overview of CPMAI Methodology
  • Business Understanding
  • Data Understanding
  • Data Preparation
  • Model Development
  • Model Evaluation
  • Model Operationalization
  • AI Projects as Data Management and Processing Projects

Who is this Training For?

  • C-Level Executives
  • Commanding Officers
  • Directors
  • Data engineer
  • Network operations
  • Information technician
  • Data technician
  • Program manager
  • Project manager
  • Supply program manager
  • Designers

Supported Learning Paths

  • Cognilytica: AI Decision-Makers, Procuring AI 
  • DoD JAIC:  Lead AI, Embed AI, Facilitate AI
  • Edison DSF: Data Science Managers (DSP01 -DSP03), Data Science Professionals (DSP04 -DSP09), Operations & Technical Support (DSP17-DSP19), Data & User Support (DSP20-DSP22)

Learning Levels

  • DoD JAIC AI: Intermediate (Applied) Level
  • Edison DSF: Level 2-3
  • Category: Implementing AI