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.

Part 1 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 Part 1 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?