Week 2 of the course focuses on the AI development lifecycle, guiding learners through the structured process required to bring an AI model from conceptualization to real-world application. This week covers the key phases of AI model creation, evaluation, deployment, and ongoing maintenance, ensuring that AI solutions align with business objectives and deliver tangible value. 


Learning Goals

  • Understand the AI Model Lifecycle: Gain insight into the structured process of AI development, from business understanding to model deployment and maintenance. 
  • Identify Business Needs for AI Implementation: Learn how to assess and define business problems that AI can address, aligning technical capabilities with strategic goals. 
  • Explore Model Selection and Development: Understand different AI models (classification, regression, clustering) and their suitability for various business applications. 
  • Learn Data Preparation and Feature Engineering Techniques: Develop skills in data cleaning, transformation, and feature selection to enhance model performance. 
  • Validate and Assess AI Models: Use performance metrics and validation techniques to ensure models generalize well and avoid overfitting. 
  • Deploy AI Models in Business Environments: Understand the steps involved in integrating AI models into operational systems and ensuring usability by end-users. 
  • Monitor and Maintain AI Systems: Explore strategies for tracking model performance, handling data drift, and continuously improving AI systems over time. 

By the end of this week, participants will have a solid foundation in managing AI projects beyond just technical development, ensuring their effective adoption and impact in business settings.