Course Overview
Get an overview of the course with a brief introduction to its general topics, intended audience, and course structure.
What is this course about?
Welcome to this course on AI Product Management, where we will explore the fascinating world of artificial intelligence (AI) and its profound impact on our lives. In today’s rapidly evolving technological landscape, AI has become a ubiquitous presence, sparking both excitement and apprehension. People have swinging perspectives on AI, from transformative technology as the fourth industrial revolution to discussing the darker side of AI.
This brings us to one of the greatest debates we find ourselves returning to with every major advancement in technology. Do we dare adopt powerful technology even when we’re aware of the risks? As far as we see it, we don’t have a choice, and the debate is only an illusion we indulge ourselves in. AI is here to stay, and nihilistic fears about it won’t save us from any harm it may cause. Pandora’s box is open, and as we peer into what remains of it, we find that hope springs eternal.
AI is holding up a mirror to our biases and inequalities, and so far, it’s not a flattering reflection. We hope that, with time, we will learn how to adopt AI responsibly in order to minimize its harm and optimize its greatest contributions to our modern civilization.
The motivation behind this course is rooted in the belief that those who create AI products play a pivotal role in shaping the future. It’s the makers of products that bring nebulous ideas into the real world. This course is designed to explore the details of how to ideate, build, manage, and maintain AI products with integrity. This course helps you to provide insights that will empower you to navigate this dynamic landscape of AI with confidence and responsibility. Welcome to the course!
Intended audience
This course is for people who aspire to be AI product managers, AI technologists, and entrepreneurs or for people who are casually interested in the considerations of bringing AI products to life. It should serve you if you’re already working in product management and have a curiosity about building products. It should also serve you if you already work in AI development in some capacity and you’re looking to bring those concepts into the discipline of product management and adopt a more business-oriented role.
While some sections in the course are more technically focused, all of the technical content in the course can be considered beginner-level and accessible to all.
What is in this course?
This course comprises the following parts:
Part 1: Lay of the land—terms, infrastructure, types of AI, and products done well
Part 2: Building an AI-native product
Part 3: Integrating AI into existing non-AI products
Part 1: Lay of the land—terms, infrastructure, types of AI, and products done well
An AI product manager needs to have a comprehensive understanding of AI, along with all the varied components that lead to its success, if they’re going to be successful in commercializing their products. This first part consists of five cumulative sections that will cover what the term AI encompasses and how to support infrastructure to make it successful within the organization. It will also cover how to support AI programs from a maintenance perspective, how to navigate the vast areas of machine learning (ML) and deep learning (DL) and choose the best path for the product, and how to understand current and future developments in AI products.
By the end of this part, we’ll understand AI terms and components, what an AI implementation means from an investment perspective, how to maintain AI products sustainably, and how to choose between the types of AI that would best fit our product and market. We’ll also learn how to understand success factors for ideating and building a minimal viable product (MVP) and how to make a product that truly serves its market. This part comprises the following sections:
“Infrastructure and Tools for Building AI Products”
“Model Development and Maintenance for AI Products”
“Machine Learning and Deep Learning Deep Dive”
“Commercializing AI Products”
“AI Transformation and Its Impact on Product Management”
Part 2: Building an AI-native product
Understanding what it takes to manage an AI program is a prerequisite when it comes to AI product management. In this part, we can move on to contextualize the outputs of that AI program in our AI-native product. This part consists of five sections addressing the various relevant areas when going to market with a new product built with AI from the very beginning.
We will cover the basics when it comes to creating an AI-native product, productizing the AI/ML service that powers it, positioning that product for various groups, AI/ML options we can take when building, and how that all relates to performance benchmarking, costs, and growth. We’ll understand both tactical and strategic considerations when launching an AI/ML-native product by the end of this part. This part comprises the following sections:
“Understanding the AI-Native Product”
“Productizing the ML Service”
“Customization for Verticals, Customers, and Peer Groups”
“Macro and Micro AI for Our Product”
“Benchmarking Performance, Growth Hacking, and Cost”
Part 3: Integrating AI into existing non-AI products
Many companies will embark on the journey of integrating AI and ML into their existing products because of the competitive advantage and strategic influence that AI has across industries. The third part of this course will focus on evolving existing products that don’t currently use ML or DL to leverage AI. In the previous part, we discussed building an AI-native product and how the process unfolds in a way that’s optimal from a data, company, and strategy perspective.
Using this same lens, we’ll now compare and contrast how this process unfolds for products that don’t currently leverage AI. By the end of this part, we’ll understand the impact and scale AI is having on individual companies and the industries they’re a part of. We'll end this part with a comprehensive guide to bringing AI/ML capabilities into traditional software products. This part comprises the following sections:
“The Rising Tide of AI”
“Trends and Insights Across Industry”
“Evolving Products into AI Products”