PRODUCT MANAGEMENT FOR AI & ML

In this introductory class, you will get to know your instructor and delve into what you can expect from this course.

• Instructor introduction
• Course objectives & flow
• AI industry overview
• GenAI vs. ML
• Key concepts & terms
• Q&A

What does an AI product manager actually do? Explore some of the day-to-day of this role, current trends shaping the AI industry, and the unique challenges of AI products. 

• What is an AI Product Manager?
• AI use cases
• Understanding AI products & lifecycle
• Navigating the hype curve
• AI industry overview
• Value frameworks for AI products

Non-Graded Assignment: Analyze an AI product in your industry. Identify its type, where it fits on the hype curve, and its business value OR Press release from the future of your industry or job, enabled by a foundational technology. 

In Class 2, you will learn to identify, frame, and prioritize business problems for AI solutions, including identifying business value and understand when and not to use AI.

• Introduce the problem
• Identifying business value
• When to use vs. when not to use AI
• Risk analysis for AI products

Assignment #1: Write up a problem in your domain that can be solved with AI, including business impact and risks. Pick ANY problem and practice articulating it. 

Discover how todevelop strategies for leveraging data in AI product development. Explore key concepts such as features, correlations, and dataset design, as well as data governance.

• Data-informed decision-making 
• Demo: Building and refining datasets for AI products
• Features, correlations, dataset design
• Data licensing, partnerships, cleanliness, warehousing, contracting, connecting
• Data governance & evaluating datasets
• Data engineering roles
• Case Study: Applying data-driven decision-making in an AI product

Non-Graded Assignment: Work with a dataset to identify features and correlations. Propose how the data could inform an AI solution.

Dive into the basics of AI and ML, including supervised vs unsupervised learning, knowledge graphs, and GenAI, to inform decision-making and collaboration with technical teams.

• AI and ML basics
• Statistics for AI product managers
• Connecting statistics to ML
• Metrics: Good, bad, & the ugly
• Workshop: Perform a simple regression or classification task using Python or Excel

Assignment #2: Analyze a dataset and propose insights for an AI solution. Work with a dataset to identify features and correlations. Propose how the data could inform an AI solution and discuss conclusions.

What is Gen AI and how does it work? In this class, you’ll build a practical understanding of generative AI concepts and develop skills in crafting effective prompts for AI systems.

• Generative AI fundamentals
• What is Generative AI?
• Tokenization, model training, & fine-tuning.
• Prompt engineering basics
• Prompt patterns & practical applications
• Workshop: Develop and test prompts to solve a business problem in your field

Non-Graded Assignment: Design a prompt for a specific use case in your industry, then test and document its effectiveness, including iterations and final results.

Explore advanced techniques for leveraging LLMs, including RAG, agentic AI, chaining, and the tools that enable these capabilities, such as video, image generation, audio, and io.

• Retrieval-Augmented Generation (RAG)
• Chaining Techniques
• Tools for Advanced LLMs
• Workshop: Build a simple RAG-based agentic flow

Assignment #3: Build an Agentic app. Evaluate its performance and document its architecture, risks, and potential improvements.

In this class, you will learn how to define success metrics for AI products and apply frameworks to mitigate risks, as well as build ethical AI solutions. 

• Success Metrics
• Aligning metrics with business outcomes
• Experimentation lifecycle
• De-risking AI Products
• Workshop: Design KPIs and an experimentation strategy for a hypothetical AI feature

Non-Graded Assignment: Create a KPI-driven experimentation plan. Develop a Responsible AI Checklist addressing privacy, fairness, and risk mitigation.

Discover how to use AI tools for market research, such as trend analysis, segmentation, and persona building, and explore tools for competitive analysis and strategy.

• AI-assisted market research
• Aligning AI insights with OKRs and value propositions
• Tools for clustering personas & analyzing competitive landscapes
• Case Study: Using AI tools for competitive analysis

Non-Graded Assignment: Perform market research for your idea - validate it using frameworks you have learned, build out a plan, and test. 

Explore key AI tools used by product managers and develop practical skills for prototyping, NPI, and creating a comprehensive AI product GTM strategy.

• Business requirements, analysis, & prioritization
• Common AI tools
• Workshop: Developing a GTM strategy
• Tools for GTM strategy

Non-Graded Assignment: Develop a GTM strategy. Bonus: Build a prototype.

Learn how to prototype AI solutions using low-code/no-code platforms such as Streamlit and Bubble, and build lightweight AI applications like chatbots, agents, and automations.

• Prototyping tools
• Lightweight AI applications
• Creating GenAI & agentic AI solutions
• Workshop: Build a functional chatbot or automation prototype

Non-Graded Assignment: Iterate on a prototype based on user feedback.

Explore the various roles in an AI solution team, stakeholder alignments, and the best practices for cross-functional communication.

• AI solution team roles
• Stakeholder mapping
• Cross-functional communication best practices
• Estimating costs, timelines, & build vs. buy decisions

Assignment #4: Turn in a pitch deck that includes research, a prototype, and plan.

This final class will equip you with career guidance, resume tips, insights from an expert guest speaker, as well as how to keep advancing your skills. Good luck! 

• Skills & qualifications
• Advancing your skills
• Resume tips
• Guest speaker Q&A