---
layout: single
title: "Machine Learning for Product Managers"
date: 2020-10-02 11:45
modified: 2020-10-02 11:45
categories: tips
tags:
  - communication
  - datascience
  - impact
  - management
  - product
---

In a [previous post](https://proinsias.github.io/tips/Communicating-Data-Science-with-impact/), I discussed the importance of learning how to properly communicate Data Science to maximize the impact of your work. Product Managers are an obvious target of this communication, and a key ally when communicating with customers.

[Alexey Kutsenko](https://www.mindtheproduct.com/profile/Alexey.Kutsenko) presents
[6 keys lessons](https://www.mindtheproduct.com/machine-learning-for-product-managers-a-quick-primer/)
for Product Managers to learn for success:

1. Develop a Nuanced Understanding of ML
2. Define the Problem You’re Trying to Solve With ML
3. Assess Whether ML is the Best way to Solve the Problem
4. Identify Mistakes and Biases
5. Get Used to Managing Uncertainty
6. Don’t Forget: Little Changes, big Consequences

Some highlights of the advice:

* "Remain user-centric and keep the focus on customer needs". Don't "fall into the trap of trying to solve all data problems with ML". "Make the product clear, coherent and understandable to less technical stakeholders."
* "Machine learning product managers must provide ML-literate specifications, ask the right questions about data, and understand what is and isn’t feasible with the available data."
* "The only reliable way to determine if an ML system is working well is to define rigorous acceptance criteria for the outputs. This involves defining quality control for the data process and results."

Add 'Machine Learning for Product Managers'
Add 'Machine Learning for Product Managers'
