AI Product Development and Lifecycle

Aim

Program Objectives

Program Structure

1. Introduction to AI Product Development
   • Overview of AI Product Development vs. Traditional Product Development
   • Types of AI Products (e.g., AI-powered applications, recommendation systems, chatbots)
   • Key Phases in AI Product Lifecycle: Ideation, Development, Deployment, Monitoring
2. Ideation and Scoping AI Products
   • Identifying Business Problems and AI Opportunities
   • Defining Product Vision, Goals, and Success Metrics
   • Market Research and Competitive Analysis for AI Products
3. Designing AI-Powered Products
   • User-Centric AI Product Design: Incorporating AI into UX/UI
   • Defining Product Features: AI-Driven vs. Non-AI-Driven Components
   • Prototyping AI Products and Validating Concepts
4. Data Strategy for AI Products
   • Data Collection, Labeling, and Management for AI Models
   • Understanding Data Requirements and Building Data Pipelines
   • Tools for Data Annotation, Versioning, and Management
5. AI Model Development and Experimentation
   • Machine Learning Model Development Cycle (Model Training, Tuning, and Testing)
   • Model Validation and Experimentation Techniques
   • Tools for Experiment Tracking (e.g., MLflow, Weights & Biases)
6. Integrating AI Models into Products
   • API Design for AI Integration
   • Microservices and Cloud-Native Architectures for AI Models
   • Choosing the Right Framework (TensorFlow, PyTorch, ONNX)
7. AI Product Deployment
   • Continuous Integration/Continuous Deployment (CI/CD) for AI
   • Model Serving and Deployment Strategies (Edge, Cloud, On-Premise)
   • Monitoring and Updating Deployed AI Models
8. Ethics and Responsible AI in Product Development
   • Ethical Considerations for AI Products (Bias, Fairness, Transparency)
   • Responsible AI Development and Governance
   • Addressing Regulatory and Legal Challenges
9. Monitoring AI Products in Production
   • Building Feedback Loops: Monitoring Model Performance in Real Time
   • Handling Model Drift and Retraining Models Post-Deployment
   • Tools for AI Model Monitoring and A/B Testing
10. AI Product Maintenance and Lifecycle Management
    • Managing Product Updates: Model Versioning and Improvements
    • Handling AI Product Evolution (Feature Updates, Performance Scaling)
    • AI Product Lifecycle Strategies (Sunsetting Products, End-of-Life Decisions)
11. Scaling AI Products
    • Scaling AI Systems for High Availability and Performance
    • Cloud Services for Scaling AI Workloads (AWS, Azure, GCP)
    • Scaling AI Products Across Global Markets and Multi-User Platforms

Who Should Enrol?

Program Outcomes

Fee Structure

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What You’ll Gain