Bratsis The AI Product Manager's Handbook

Develop a product that takes advantage of machine learning to solve AI problems
1. Auflage 2023
ISBN: 978-1-80461-733-5
Verlag: De Gruyter
Format: EPUB
Kopierschutz: 0 - No protection

E-Book, Englisch, 250 Seiten

Product managers working with artificial intelligence will be able to put their knowledge to work with this practical guide to applied AI. This book covers everything you need to know to drive product development and growth in the AI industry. From understanding AI and machine learning to developing and launching AI products, it provides the strategies, techniques, and tools you need to succeed.
The first part of the book focuses on establishing a foundation of the concepts most relevant to maintaining AI pipelines. The next part focuses on building an AI-native product, and the final part guides you in integrating AI into existing products.
You'll learn about the types of AI, how to integrate AI into a product or business, and the infrastructure to support the exhaustive and ambitious endeavor of creating AI products or integrating AI into existing products. You'll gain practical knowledge of managing AI product development processes, evaluating and optimizing AI models, and navigating complex ethical and legal considerations associated with AI products. With the help of real-world examples and case studies, you'll stay ahead of the curve in the rapidly evolving field of AI and ML.
By the end of this book, you'll have understood how to navigate the world of AI from a product perspective.

Autoren/Hrsg.

Bratsis, Irene

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Datenbankdesign & Datenbanktheorie
• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Informationsarchitektur

Inhaltsverzeichnis

Table of Contents - Understanding the 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
- Understanding the AI-Native Product
- Productizing the ML Service
- Customization for Verticals, Customers, and Peer Groups
- Macro and Micro AI for Your Product
- Benchmarking Performance, Growth Hacking, and Cost
- The Rising Tide of AI
- Trends and Insights across Industry
- Evolving Products into AI Products

Leseproben

1

Understanding the Infrastructure and Tools for Building AI Products

Laying a solid foundation is an essential part of understanding anything, and the frontier of artificial intelligence (AI) products seems a lot like our universe: ever-expanding. That rate of expansion is increasing with every passing year as we go deeper into a new way to conceptualize products, organizations, and the industries we’re all a part of. Virtually every aspect of our lives will be impacted in some way by AI and we hope those reading will come out of this experience more confident about what AI adoption will look like for the products they support or hope to build someday.

of this book will serve as an overview of the lay of the land. We will cover terms, infrastructure, types of AI algorithms, and products done well, and by the end of this section, you will understand the various considerations when attempting to build an AI strategy, whether you’re looking to create a native-AI product or add AI features to an existing product.

Managing AI products is a highly iterative process, and the work of a product manager is to help your organization discover what the best combination of infrastructure, training, and deployment workflow is to maximize success in your target market. The performance and success of AI products lie in understanding the infrastructure needed for managing AI pipelines, the outputs of which will then be integrated into a product. In this chapter, we will cover everything from databases to workbenches to deployment strategies to tools you can use to manage your AI projects, as well as how to gauge your product’s efficacy.

This chapter will serve as a high-level overview of the subsequent chapters in but it will foremost allow for a definition of terms, which are quite hard to come by in today’s marketing-heavy AI competitive landscape. These days, it feels like every product is an AI product, and marketing departments are trigger-happy with sprinkling that term around, rendering it almost useless as a descriptor. We suspect this won’t be changing anytime soon, but the more fluency consumers and customers alike have with the capabilities and specifics of AI, machine learning (ML), and data science, the more we should see clarity about how products are built and optimized. Understanding the context of AI is important for anyone considering building or supporting an AI product.

In this chapter, we will cover the following topics:

• Definitions – what is and is not AI
• ML versus DL – understanding the difference
• Learning types in ML
• The order – what is the optimal flow and where does every part of the process live?
• DB 101 – databases, warehouses, data lakes, and lakehouses
• Managing projects – IaaS
• Deployment strategies – what do we do with these outputs?
• Succeeding in AI – how well-managed AI companies do infrastructure right
• The promise of AI – where is AI taking us?

Definitions – what is and is not AI

In 1950, a mathematician and world war II war hero Alan Turing asked a simple question in his paper – . Today, we’re still grappling with that same question. Depending on who you ask, AI can be many things. Many maps exist out there on the internet, from expert systems used in healthcare and finance to facial recognition to natural language processing to regression models. As we continue with this chapter, we will cover many of the facets of AI that apply to products emerging in the market.

For the purposes of applied AI in products across industries, in this book, we will focus primarily on ML and deep learning (DL) models used in various capacities because these are often used in production anywhere AI is referenced in any marketing capacity. We will use AI/ML as a blanket term covering a span of ML applications and we will cover the major areas most people would consider ML, such as DL, computer vision, natural language processing, and facial recognition. These are the methods of applied AI that most people will come across in the industry, and familiarity with these applications will serve any product manager looking to break into AI. If anything, we’d like to help anyone who’s looking to expand into the field from another product management background to choose which area of AI appeals to them most.

We’d also like to cover what is and what isn’t ML. The best way for us to express it as simply as we can is: if a machine is learning from some past behavior and if its success rate is improving as a result of this learning, it is ML! . No models are perfect but we do learn a lot from employing models. Every model will have some element of hyperparameter tuning, and the use of each model will yield certain results in performance. Data scientists and ML engineers working with these models will be able to benchmark performance and see how performance is improving. If there are fixed, hardcoded rules that don’t change, it’s not ML.

AI is a subset of computer science, and all programmers are effectively doing just that: giving computers a set of instructions to fire away on. If your current program doesn’t learn from the past in any way, if it simply executes on directives it was hardcoded with, we can’t call this ML. You may have heard the terms or thrown around in other programs. They are considered forms of AI, but they're not ML because although they are a form of AI, the rules are effectively replicating the work of a person, and the system itself is not learning or changing on its own.

We find ourselves in a tricky time in AI adoption where it can be very difficult to find information online about what makes a product . Marketing is eager to add the AI label to their products but there still isn’t a baseline of explainability with what that means out in the market. This further confuses the term AI for consumers and technologists alike. If you’re confused by the terms, particularly when they’re applied to products you see promoted online, you’re very much not alone.

Another area of confusion is the general term that is AI. For most people, the concept of AI brings to mind the franchise from the 1980s and other futurist depictions of inescapable technological destruction. While there certainly can be a lot of harm to come from AI, this depiction represents what’s referred to as or artificial general intelligence (AGI). We still have ways to go for something such as AGI but we’ve got plenty of what’s referred to as artificial narrow intelligence or narrow AI (ANI).

ANI is also commonly expressed as and is what’s generally meant when you see AI plastered all over products you find online. ANI is exactly what it sounds like: a narrow application of AI. Maybe it’s good at talking to you, at predicting some future value, or at organizing things; maybe it’s an expert at that, but its expertise won’t bleed into other areas. If it could, it would stop being ANI. These major areas of AI are referred to as strong and weak in comparison to human intelligence. Even the most convincing conversational AIs out there, and they are quite convincing, are demonstrating an illusionary intelligence. Effectively, all AI that exists at the moment is weak or ANI. Our days are still firmly in our future, perhaps never to be realized.

For every person out there that’s come across Reddit threads about AI being sentient or somehow having ill will toward us, we want to make the following statement very clear. AGI does not exist and there is no such thing as sentient AI. This does not mean AI doesn’t actively and routinely cause humans harm, even in its current form. The major caveat here is that unethical, haphazard applications of AI already actively cause us both minor inconveniences and major upsets. Building AI ethically and responsibly is still a work in progress. While AI systems may not be sentiently plotting the downfall of humanity, when they’re left untested, improperly managed, and inadequately vetted for bias, the applications of ANI that are deployed already have the capacity to do real damage in our lives.

For now, can machines think like us? No, they don’t think like us. Will they someday? We hope not. It’s my personal opinion that the insufferable aspects of the human condition end with us. But we do very much believe that we will experience some of our greatest ails, as well as our wildest curiosities, to be impacted considerably by the benevolence of AI and ML.

ML versus DL – understanding the difference

As a product manager, you’re going to need to build a lot of trust with your technical counterparts so that, together, you can build an amazing product that works as well as it can technically. If you’re reading this book, you’ve likely come across the...

Über Autor(innen)

Bratsis Irene:

Irene Bratsis is a director of digital product and data at the International WELL Building Institute (IWBI). She has a bachelor's in economics, and after completing various MOOCs in data science and big data analytics, she completed a data science program with Thinkful. Before joining IWBI, Irene worked as an operations analyst at Tesla, a data scientist at Gesture, a data product manager at Beekin, and head of product at Tenacity. Irene volunteers as NYC chapter co-lead for Women in Data, has coordinated various AI accelerators, moderated countless events with a speaker series with Women in AI called WaiTalk, and runs a monthly book club focused on data and AI books.