Online/ e-LMS
Self Paced
Moderate
3 Weeks
About
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.
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.
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.
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
Participant’s Eligibility
Product managers, AI engineers, data scientists, and entrepreneurs interested in AI-driven product development.
Program Outcomes
- Ability to oversee the complete lifecycle of AI product development.
- Proficiency in building and deploying AI models that solve real-world problems.
- Understanding of how to integrate, scale, and monitor AI products post-launch.
- Skills in aligning AI products with business objectives and user needs.
Fee Structure
Standard Fee: INR 4,998 USD 78
Discounted Fee: INR 2499 USD 39
We are excited to announce that we now accept payments in over 20 global currencies, in addition to USD. Check out our list to see if your preferred currency is supported. Enjoy the convenience and flexibility of paying in your local currency!
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Certificate
Program Assessment
Certification to this program will be based on the evaluation of following assignment (s)/ examinations:
| Exam | Weightage |
|---|---|
| Mid Term Assignments | 50 % |
| Project Report Submission (Includes Mandatory Paper Publication) | 50 % |
To study the printed/online course material, submit and clear, the mid term assignments, project work/research study (in completion of project work/research study, a final report must be submitted) and the online examination, you are allotted a 1-month period. You will be awarded a certificate, only after successful completion/ and clearance of all the aforesaid assignment(s) and examinations.
Program Deliverables
- Access to e-LMS
- Real Time Project for Dissertation
- Project Guidance
- Paper Publication Opportunity
- Self Assessment
- Final Examination
- e-Certification
- e-Marksheet
Future Career Prospects
- AI Product Manager
- AI Product Development Engineer
- AI Solutions Architect
- AI Strategy Consultant
- AI Business Analyst
- AI Product Lead
Job Opportunities
- AI-focused startups and enterprises developing AI-based consumer and enterprise products
- Technology and software companies creating AI tools for industries like healthcare, finance, and retail
- Organizations integrating AI into their digital transformation strategies
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