What Are Data and AI Products in the Real World?
It’s often confusing to define what really makes a Data or AI Product. This article breaks it down into false friends, enablers, and product families.
Real-World Practices 🏗️ |
In this section, we move from theory to practical delivery, where Data & AI gradually transform into real-world solutions that teams can rely on in their daily work.
We focus on how to build, deploy, and scale robust products that consistently deliver measurable business impact.
From scalable pipelines to advanced analytics, and generative copilots, you’ll find concrete actionable insights, hands-on tutorials, and valuable lessons learned directly from the field.
What You’ll Find Here
- ⚙️ Principles & Patterns: Explore the guiding principles, reusable patterns, and mental frameworks that help distinguish artifacts from true products, turning experiments into solutions that last.
- 📊 Showcases: See real examples of Data & AI products in action: dashboards, predictive engines, and generative assistants. No theory, just tangible outputs that illustrate the value delivered to end users.
- 🛠️ Hands-On Tutorials: Step into the how-to: build and deploy products yourself, from a BI dashboard to a production-ready ML model or a generative chatbot powered by LLMs.
- 📖 Case Studies: Dive into stories from the field: how companies implemented fraud detection, scaled recommendation engines, or navigated the pitfalls of deploying AI at scale, with both successes and failures as lessons.
Mapping the Landscape of Data & AI Products
The diagram shows three product families: Analytics/BI to describe the past, Predictive/Prescriptive to anticipate outcomes, and Generative/Agentic to create and act.
These rely on enablers: Data as a Product for trust and usability, and Data Platforms for storage, transformation, and scale.