AI Product Development and Lifecycle

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Mentor Based

Program Syllabus

Module 1

About This Course

This program covers the complete development process for AI products, including product scoping, AI model development, system integration, testing, deployment, and post-launch monitoring. Participants will learn to manage AI teams, collaborate across departments, and ensure product-market fit while focusing on scalability and ethical considerations.

Module 2

Aim

To equip professionals with advanced knowledge of the full lifecycle of AI product development, from ideation to deployment and maintenance. This course is designed to help participants create scalable, AI-driven products aligned with business goals.

Module 3

Program Objectives

Understand the AI product lifecycle from concept to deployment.
Develop data strategies and build AI models for product development.
Design, test, and scale AI products while maintaining product-market fit.
Learn the nuances of ethical AI product development.
Gain insights into post-launch optimization and model updates.

Module 4

Program Structure

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
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
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
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
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)
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)
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
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
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
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)
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

Lead Instructor

Dr. Sarah Chen

PhD in Computational Mechanics from MIT with 15+ years of experience in Industrial AI. Former Lead Data Scientist at Tesla and current advisor to Fortune 500 manufacturing firms.