Programme Parameters
Advanced Postgrad
3 Weeks (30 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
AI Product Management is a comprehensive, industry-oriented course tailored for aspiring product managers, entrepreneurs, and technologists. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
AI Product Management is a comprehensive, industry-oriented course tailored for aspiring product managers, entrepreneurs, and technologists.
📋 Course Objectives
1. Translate AI Enablement theory into practical, reproducible analysis.
2. Build a defensible project you can showcase to supervisors, reviewers, or employers.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in AI Enablement
• R&D engineers and working professionals applying AI Enablement in industry
• Academics and educators building research or teaching capacity in AI Enablement
🚀 Key Learning Outcomes
• Tangible, reproducible AI Enablement work to show supervisors or employers.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Foundations of AI Product Management
Define the pivotal role of an AI Product Manager. • Distinguish AI products from traditional software solutions. • Outline the complete AI Product Lifecycle from concept to deployment.
Fundamentals of AI & Machine Learning for PMs
Grasp core AI/ML concepts essential for product management. • Explore various model types: Supervised, Unsupervised, and Generative AI. • Understand the critical data lifecycle and its product impact. • Analyze evaluation metrics and make informed trade-offs.
Designing & Scoping AI Solutions
Identify compelling AI use cases with market potential. • Scope Minimum Viable Products (MVPs) for AI capabilities. • Manage data acquisition, labeling, and annotation processes. • Collaborate effectively with data scientists on model selection.
AI Product Design & User Experience
Design for explainability, trust, and user comprehension. • Implement Human-in-the-Loop (HITL) design patterns. • Establish robust feedback loops and active learning systems. • Address ethical considerations in AI interface design.
AI Product Delivery & Operations
Master model deployment workflows and leverage essential tools. • Execute A/B testing and establish effective monitoring for AI systems. • Manage model drift, retraining, and continuous updates. • Integrate MLOps practices and select appropriate platform choices.
AI Product Strategy & Leadership
Develop a compelling AI product strategy aligned with business goals. • Create strategic roadmaps for data-driven product development. • Lead and manage cross-functional AI product teams. • Navigate legal, compliance, and risk frameworks in AI.
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | MLOps platforms |
| Covered Tool / Platform | A/B testing tools |
| Covered Tool / Platform | Data labeling tools |
| Covered Tool / Platform | Machine Learning models (Supervised |
| Covered Tool / Platform | Unsupervised |
| Covered Tool / Platform | Generative) |
Frequently Asked Questions
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