Slowly more and more products are incorporating elements of Machine Learning and AI. Product Managers need to understand what the technologies are about and their impact. These articles ease you gently into the role of a product manager and provide some great tips.
As Martin Zinkevich’s document on best practices for ML engineering describes, you should not be afraid to ship a product that does not use machine learning.
Many products can collect useful customer feedback using simple baselines; in the document, Martin quotes an example of sorting apps in an app store by download count.
Even simple machine learning projects can be difficult, and managing these projects in a real business is much harder than most people realize; that’s why Venture Beat claims 87% of machine learning products never make it into production, and Harvard Business Review says that “The first wave of corporate AI is bound to fail.” Machine learning is not fairy dust you can sprinkle on your existing product.
In “The AI Hierarchy of Needs,” Monica Rogati argues that you can build an AI capability only after you’ve built a solid data infrastructure, including data collection, data storage, data pipelines, data preparation, and traditional analytics.
Start with a simple project, build your infrastructure, learn how to use your data effectively.
Assuming that the selected machine learning technique is suitable, the product manager will have to make several important decisions about the model.
A product manager must decide whether to refactor the research code, determine the scope of the ML model’s inference engine, decide on model format , ensure that the modeling technique can support the service level agreement of the AI system, and plan for deployment and maintenance.
The foundation of any data product consists of “solid data infrastructure, including data collection, data storage, data pipelines, data preparation, and traditional analytics.” A product manager for this phase prepares the way for putting products into production by building the infrastructure needed to support the design, development, and use of future products.
So what did I learn? Start a Machine Learning product without using ML! And manage expectations carefully (your own and others). But the most essential learning is that we should understand the technologies and their current state of development. Building ML products is a lot of work.
I first encountered the third article, read it, and followed two links and read the other two.
I also tried a small experiment and cheated a bit. I fed each one article to a text summarizer, reviewed these summaries and took portions of them to use as context.
The three quoted blocks above were written by a Machine Learing app!