Technology

Why Product Managers Should Know Machine Learning

This curated post is authored by Vishal Sood, Senior Product Manager, Amazon

product manager machine learning

Machine learning is going to change the world more than any other technology, over the next several decades. To take advantage of the machine learning revolution we (aka product managers) should move quickly to equip ourselves with the necessary tools. Or else, we would be lost in obscurity bitting the dust, because many top technology companies are already harnessing ML to create new business opportunities.

As Mark Cuban recently stated:

“Artificial Intelligence, deep learning, machine learning — whatever you’re doing if you don’t understand it — learn it. Because otherwise you’re going to be a dinosaur within 3 years.”

Here is why I believe every Product Manager should understand Machine Learning and how can they get a jump start on it.

The Why ?

For a moment, pause and think of all the products that you use on a daily basis. I bet they all use some flavor of machine learning in their products. Here are a few that I am guessing we all use almost everyday:

  1. Google, trying to answer your query with the best possible results ? This is a ranking problem — helping users find the right thing when they search.
  2. Netflix or Spotify suggesting movies or songs to customers that they might be interested in? This is a recommendation problem — giving users things they may be interested in, without them explicitly searching.
  3. Zillow predicting your house prices? This is a regression problem — predicting a numerical value of a thing.
  4. Gmail marking an email as spam/not spam? This is a classification problem — figuring out what kind of thing something is.
  5. Facebook photos detecting faces? This is a also classification problem.
  6. Amazon’s world famous “customers who bought this also bought this” ? This is clustering in action — Putting similar things together.

And the list can go on, but I guess you get my point. Machine learning is everywhere. Machine learning is going to upend industries and your product. If I can go a bit further, it will be the next big tech platform where incumbents will be upended and new leaders emerge (If you think ML is a fad, check in with someone who thought the web was a fad in 1998, or mobile in 2008). Hence, as product managers we need to understand the current state of machine learning, and the opportunities and techniques such as deep learning provide for transforming your products and delighting your users.

By ‘understanding machine learning’ I don’t mean that you have in-depth knowledge of ML algorithms that you should be able come up with models for the algorithms (it will be good if you can, but that’s besides the point).

But, you should know enough to answer these questions below:

  • How much value Machine learning is going to add to your product ?
  • What can Machine learning achieve for your product, and what would it take to execute it (more data, better algorithm, etc) — in technical sense
  • Identify difference between hype and real-achievable things from Machine learning.

And then, when you are working on a product that uses Machine learning (which will be very often), you should make sure to understand:

  • That there are lot many ways by which Machine learning can create illusion of being right when it is not — learn to capture that.
  • How much sample of data will be needed so that your predictions can hold true in real world.
  • That preparation of data will account of 80–90% of a developer’s time

The How?

A simple google search on ‘machine learning for beginners’ can give you more than a million results. To make things easier, with the help of some of my friends at Amazon, I have curated a list of the best resources to learn about machine learning esp. for Product Managers

Books

Courses

Must-Reads:

Other:

This is in no way an exhaustive list and I will keep updating it as and when I come across more valuable resources. All product managers, I would love to know your take on ML and how it is impacting your products. Please leave a comment below.


Disclaimer: This is a curated post. The statements, opinions and data contained in this column are solely those of the individual authors and contributors and not that of iamwire or its editor(s). The article in its original form was published by the author here.

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