When and how do I add Machine Learning to a product roadmap?

When and how do I add Machine Learning to a product roadmap? – Not every product needs a Machine Learning-based solution. This week, Kenlyn Terai offers a framework for assessing if ML is right for your business needs, then outlines how you can go about incorporating an ML-based solution into your product roadmap.

by Women in Product

When and how do I add Machine Learning to a product roadmap?

Introduction

Are you looking for ways to improve personalization, natural language processing (NLP), or search customization? Machine Learning (ML) could be the tool you need. This article will describe what Machine Learning is, what problems it can solve for you, and how to incorporate this toolkit into your product roadmaps. More importantly, this article also explains when machine learning is not the right tool for the job.

Note: There are several steps needed to take Machine Learning from concept to execution, including data preparation, model development, and the actual deployment processes. While product managers need to be actively involved in these stages, these are topics that — for now — are outside the scope of this article.

What is Machine Learning?

Arthur Samuel, a pioneer of AI research, provides us with a concise definition: “Machine Learning is the field of study that gives computers the ability to learn without being explicitly programmed.” Where traditional programming requires us to provide data and rules to generate answers, Machine Learning requires us to provide desired answers and data to generate rules.

In academics, Machine Learning is a subset of Data Science, the study of data which involves developing methods of recording, storing, and analyzing data to effectively extract useful information.

Artificial Intelligence > Machine Learning > Deep Learning

Machine Learning (ML) is sometimes confused with Artificial Intelligence (AI) and Deep Learning (DL), which are related but differentiated. Whenever a machine completes tasks based on a set of stipulated rules that solve problems (i.e., algorithms), such an “intelligent” behavior is what is called Artificial Intelligence and includes ML and DL. ML is a subset of AI, and DL is a subset of ML. The progression from AI through DL generally represents more human-independent rule definitions and, as such, require more and more data. The diagram below shows where ML and DL fall within the AI discipline.

When is a Machine Learning-based solution appropriate?

Problems that are well suited for ML-based solutions

Good use cases for Machine Learning include problems that require personalization, ranking, classification, regression, clustering or identifying anomalies. Note that the type of problem you want to solve will drive the choice of algorithm to use (e.g. for clustering, you would use k-means).

In general, for Machine Learning to make sense for a business, your problem should have these characteristics:

  • Requires complex logic that’s impractical to solve with human-defined rules, or heuristics. For example, search engines often have multiple phases of ranking that happen in series, such as initial retrieval, primary ranking, contextual ranking, and personalized ranking. This is a great application for Machine Learning.
  • The problem will be scaling up very fast. If you expect that your problem will scale to thousands of users or more, then it could be a good use case for ML. Let’s say you have an online retail platform and expect to have thousands of customers avail of your new offer within three to six months. Retail customer expectations being what they are, you need a personalized experience and could give a good justification for ML.
  • Requires personalization at scale. Unless you can reduce the complexity of the problem space by, for example, creating solutions for a particular segment or category rather than for each individual, you’re better off using ML to define the rules you use.
  • Require rules that change quickly over time. If your rules generally remain static year after year, then a heuristic solution is preferred. However, if your business’s success depends upon quick adaptation and rule changes, then ML is a good route. For example, if you have a search product and Ed Sheeran drops a new album, your algorithm needs to adapt in real-time and is amenable to an ML solution.
  • Has a known, pre-defined end result. For example, in online retail, you want your model to provide recommendations which result in a sale. In search, typing “shirt” should return results with lists of shirts that are most likely to lead to a purchase.
  • Does not require 100% accuracy. If business success can be achieved with a high probability of accuracy rather than with perfection, then ML is a good option. For example, recommendation systems will not be considered faulty if users don’t always want what is served. Users can still have a great experience and the program can learn from the lack of sales to deliver improved recommendations in the future.

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