The Step-By-Step PM Guide to Building Machine Learning Based Products
What Product Managers Need to Know About Machine Learning Is Science, but Not Rocket Science
It’s time for every product manager, entrepreneur or business leader to get up to speed on machine learning. Even if you’re not building the next chatbot or self driving car, you’ll probably need to use machine learning in your product sooner rather than later to stay competitive. The good news is you don’t need to invent the technology (though kudos if you do), just leverage what already exists. Tech companies have open sourced tools and platforms (Amazon AI, TensorFlow, originally developed by Google, and many others) that make machine learning accessible to virtually any company today.
When I started in machine learning I knew next to nothing about it, yet in a relatively short time I was leading the development of products with machine learning at their very core (such as this). My goal is to give you a good enough understanding of both the technology and the process of developing ML products to get you started quickly. This is a step-by-step guide to becoming an effective PM in an organization that leverages machine learning to achieve business goals.
While ML is an incredibly technical space, many of the fundamentals you need to understand to maximize business impact have little to do with developing complex algorithms. They’re about ensuring you ask the right questions, understand the process of developing ML models, and structure an organization that fosters constant collaboration between disciplines rather than treating data science (the organization creating those models) as a “black box” that will magically generate insights.
This tutorial has 6 parts:
My goal is to illustrate core concepts that are broadly applicable and form a basis from which you can grow your knowledge in the areas that are most relevant to your business; therefore there may be cases where I’m oversimplifying or not addressing all possible applications or aspects of the science for the sake of clarity.
Let’s get started with part 1: What Machine Learning Can Do for Your Business and How to Figure It Out.
Many thanks to Gil Arditi, Yael Avidan, Eran Davidov and Gal Gavish for their invaluable feedback, and special thanks to Arvind Ganesan who taught me so much of what I know about machine learning. Any mistakes are entirely my own.
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