#4 AI Development Lifecycle Overview and Business Goal Definition

ℹ️ Overview

This lesson focuses on the practicalities of the AI development lifecycle, emphasizing its creative and iterative nature. Omar Shanti walks through the steps required to effectively implement AI solutions in a business, highlighting the importance of starting with clear goals, leveraging accessible data, and delivering quick wins to build momentum and justify further investment.

📒 Key Takeaways

• The Development Lifecycle is Creative and Iterative
  The AI development process can be messy but highly creative. Embracing this complexity is crucial for success.
  
  

• Start with a Clear Business Goal

  • Identify a business problem backed by ROI (return on investment).
  • Clearly define the problem to be solved and determine success metrics upfront.
    
    

• Frame the Problem as an ML Challenge

  • Once the business goal is clear, translate it into a machine learning (ML) problem.
  • Determine the data required to address the problem.
    
    

• Data Preparation is Key

  • Gather and prepare the necessary data to build the foundation of your model.
    
    

• Build, Deploy, and Monitor

  • Create the AI model and establish workflows for its use.
  • Deploy the model and set up systems to monitor performance. This ensures detection of issues like bias or inaccuracies.
    
    

• Focus on "Low-Hanging Fruit" First

  • Start with simple projects using readily available data to demonstrate the value of AI.
  • Build "champions" within the company by proving AI’s effectiveness quickly, justifying future investments.