There’s an evolution in product management correlated with the shift from the digital revolution to the AI revolution. AI product managers are rising. As AI and ML eat software, more and more PMs and execs need to level up their skills to manage these products and provide requirements and specifications that add value to the data engineering and data science teams. This leads to actually solving customer pain points and not just building a cool technology solution.

Imagine a new recommendation feature. Traditionally, a PM would work with a designer and come up with the UI/UX specification around the layout, when and where to show the recommendations, the behavior on interacting with the recommendations, etc. However, product specifications around UX, layout, or interactions are of no value to a machine learning engineer or a data scientist who would need to operationalize a machine learning system to power such recommendations.

Mayukh Bhaowal (Salesforce) details the top areas an AI product manager needs to focus on above and beyond a traditional PM, including mapping of business problems to machine learning problems, understanding data and labelled data nuances, defining crisp model evaluation criteria, model explainability, ethics and bias, and the distinction between research and production when it comes to AI-powered products and features.

Prerequisite knowledge

• A working knowledge of AI product space

What you'll learn

• Understand that the role of product management has changed fundamentally and that AI product managers and execs need unique skill sets above and beyond traditional PMs to be successful in the real world building AI/ML products
• Learn how AI product managers and execs need to think differently about customers, use cases, and how to translate them into tangible requirements for data engineers and data scientists

This session is from the 2019 O'Reilly Artificial Intelligence Conference in San Jose, CA.