Since 2006, I have worked within companies that have integrated AI (or machine learning or data science) into their solutions. These companies have ranged in size from micro start-ups all the way to large multi-nationals. As to their level of success, I will let their board members and customers be the judge of that. This article is a group of things that I wish I had felt able to say to the various business leaders in those companies. I am writing this as as open letter, as it felt like the right way to do it.

Photo by Green Chameleon on Unsplash

Dear Leadership,

You say…


Sprint 3 — Is Data Science an Oxymoron?

This post is the third in a series about being an ML PM, that started here.

When I first learned about science, way back in elementary school, it was presented as a series of facts. As part of the learning process, students would perform some experiments to gather observations that would allow us to further understand the facts.

The scientific concepts of theory and hypothesis were not introduced until much much later. (Sadly, I think, too late for many people to understand that the scientific body of knowledge changes, leading to a…


Sprint 2 — Do what you do

This post is the second in a series about being an ML PM, that started here.

As a product manager you are all about relationships. You don’t actually manage anyone, unless you are the lead/head/director PM. But to do your daily tasks, you are an influencer, a listener, and interpreter.

As an experienced PM, you have already mastered this in

  • the business space (selling your features up),
  • the customer space (eliciting feedback) and
  • in the Dev space (getting your stories in the sprint).

Now there is one more space: Data Science.

The last…


Sprint 1 — The flip side

My other series focusses on sharing tips and concepts with data scientists who want to learn more about product. I’m giving that POV a break for a moment, and sharing my thoughts on how to transition from a product manager to an ML product manager.

I started working in a product-focused company immediately after earning my PhD. The job was with a company that was new to incorporating ML into their software. Lucky for me, this was my second career and so I had maturity that helped deal with some difficult discussions. I have…


Sprint 5 — Done yet?

This is a continuation of a series that started here. The article is based on my own experience as an ML PM, yours may be different.

Scientists are curious individuals. In my experience, we also tend to be perfectionists. Especially afflicted are those that have a PhD.

Software products are developed in an iterative way. Design, build, QA, and then release. Repeat. Every release will have bugs. And, every release will have room for improvement.

These two realities may be at odds with each other.

As a data scientist in a product team, it is…


Sprint 4 — Product Integration

This is a continuation of a series that started here. The article is based on my own experience as an ML PM, yours may be different.

The last article was all about the documentation required to assist in productizing your work. What does it mean to productize something? And what about ML in a product?

The process of creating something new is messy and often has unexpected challenges.
The process of creating something new is messy and often has unexpected challenges.

Products are a special consumer of ML. When I start working with a data scientist that is not familiar with product, I try to teach them about the most important concepts for the integration of ML:

  • Automated — The models…

Sprint 3 — The Docs

This is a continuation of a series that started here. The article is based on my own experience as an ML PM, yours may be different.

During sprint 2, I wrote about the product team from from the perspective of a software company that uses ML to implement specific features. I hope that I managed to convey the following:

The development team writes code to create a maintainable, optimized software product using a process to ensure an acceptable level of quality. These objectives are different from yours.

What does this mean? Well, most likely (in…


Sprint 2 — The Team

I am an ML product manager sharing my perspective based on my own experience in software products. This is part of series to explain to aspiring Data Science professionals what is like to work in a product role. The start of the series is here.

As cliché as it is, no one does it alone. Not even the super techie with the unbelievably good idea that makes a bazillion dollars. To get a great idea into a product that will make customers glad that they bought it, takes a lot of talented people. With respect to this article, The Team…


Sprint 1 — Intro

I am an ML product manager. What does that mean? Well, I do all the regular PM stuff such as:

  • talk to customers,
  • identify requirements,
  • write specs and stories,
  • prioritize features,
  • work with Dev and QA to implement the feature…

Also, I have a technical background that includes image and signal processing, machine learning, engineering. Prior to working as a PM, I worked as a data scientist (DS), before it was commonly called that. Which means I also:

  • consider what problems should have ML in its solution,
  • work with DS to make their work productizable,
  • work with Dev on ML…

Tyna Hope

An ML product manager. Bio on LinkedIn. Opinions expressed are my own.

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