Introduction to Productizing the ML Services

What is in this section?

In the previous chapter, we briefly discussed the notion of productizing and what that means for AI outputs in the “Productizing AI-Powered Outputs” lesson. We will be expanding on that concept in this section by exploring the trials and tribulations that may come up when building an AI product. Rather than thinking of AI products as traditional software products, it helps to think of them as a service that we’re learning to productize. What this refers to is the ability to create a consistent workflow that we can rely on to deliver consistent results in the way traditional products demand.

We will be going more in-depth into product management principles and aligning them to the idiosyncrasies of AI/ML services.

By the end of this section, we will have an understanding of the following topics:

• “AI vs. Traditional Software Products”

• “Differences Between AI and Traditional Software Products”

• “B2B vs. B2C—Productizing Business Models”

• “Experimentation”

• “Consistency and AIOps/MLOps”

• “Performance Evaluation”

• “Feedback Loop”