How managing a product with ML is different from managing traditional products

Image credit: Deepak Pal CC 2.0

Managing a product that leverages Machine Learning (ML) is quite different from managing a traditional non-ML product. In this post, I will highlight a few key differences that I observed so far in my career. You might find this useful if you are switching to an ML Product Manager role or if you are simply curious about the differences.

1. Relentlessly focus on the customer problem

Ok, this is not an ML product specific advice. It’s a classic Product Management mantra. Define the customer problem first — “what”, “why” and “who” before diving into the “how”.

But ML product managers are especially prone to the fallacy of…

IBM Watson Natural Language Understanding offers out of the box categorization with a taxonomy of over a thousand categories. For users looking to categorize content beyond the thousand categories; our team is excited to announce the experimental release of Custom Categories in Watson Knowledge Studio, Natural Language Understanding, and Discovery.

Model customization requires little to no training data. The only information you need is category labels and key phrases uniquely identifying each category. At the moment, the underlying algorithm for training the model uses Wikipedia.

Customer Pain Point

Let’s take a use case of categorizing a list of products like the one below.

One of the fundamental tasks in the field of Natural Language Processing (NLP) involves breaking down content into its smallest possible units, understanding the meaning of each unit, and using that information to build higher order features such as Named Entity Recognition and Sentiment Analysis.
Watson Natural Language Understanding (NLU) applies a suite of such NLP tasks for supporting features like Keywords extraction.

Today we are releasing these building blocks of language understanding as part of a new Syntax API within NLU. This is a free experimental feature with support for the English language at the moment. …

Twitter is a great source of data for analyzing real-time trends related to a topic of your interest. You can collect tweets that mention a certain brand, event or a person and start to see a pattern emerge. You can perform Sentiment analysis on tweets to determine if a certain brand or event is trending positive or negative in the social network.

This step by step tutorial will show how to collect tweets related to a topic of your interest, determine the sentiment of the text in the tweet, and store the results in a Google spreadsheet for further analysis.

Whether you are a business owner or an employee of a company, you notice large amounts of unstructured data being generated every day pertaining to the business. Sources for this data can be internal, such as emails, instant messages, documents, and wikis, as well as external, such as social media, news, and blogs.

There must be times when you ask yourself — how can I make sense of all this data, and use it as an advantage for the business?

You can invest significant time and resources, form a team of Natural Language, Data Science, and Machine Learning engineers, and…

Pavan Tummala

Product @ Splunk. Previously IBM Watson.

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store