Not Another AI talk - A PM's practical tips to building AI Products by Navdeep Martin

1 article/video left!

log in or sign up to unlock 3 more articles/videos this month and explore our expert resources.

Automatic Summary

Building Successful AI Products: A Comprehensive Guide For Product Managers

Artificial Intelligence (AI) product development can be both thrilling and challenging, and as a Product Manager you are at the forefront of these exciting ventures. In this article, I will share some valuable insights and tips I've gathered over the course of my career and hopefully guide you on the path to building successful AI products.

About Me

My name is Navdeep Martin, an experienced AI product manager with a computer science background. I honed my skills in machine learning product management at prestigious organizations like the CIA, Washington Post, and Comcast before moving on to leading AI-driven start-ups. Some of the roles I've held include AI strategy leadership at Primer AI and, more recently, VP of Product at Blackbird AI. My experience in building AI products across diverse sectors has afforded me a wealth of knowledge that I'm eager to pass on to other AI enthusiasts.

Understanding Key AI Terms

To ensure we're all on the same page, let's start with a quick look at some commonly used AI and machine learning terminology.

  • Machine Learning: This is a branch of AI where computers learn from given examples of data to make future predictions. For instance, a machine learning model deployed in a news recommendation module determines the optimal news article recommendations for users to trigger a click.
  • Training Data: Inputs used to teach a machine learning model to predict or classify information properly. A typical scenario includes having a subject matter expert tag articles with related categories (fashion, parenting, national security, etc.). This tagged data becomes the training information the machine learning model uses to classify future articles.

AI and machine learning are crucial components of the digital world, with applications in various domains, including personalization programs like tailored grocery store app recommendations, personalized Pinterest feed, and image analysis for brand and risk detection.

Five Key Tips for AI Product Management

  1. Design Matters: Pay attention to how sizeable your texts are, your headings' names, and most importantly, how your customers will interact with your product. Involve your designer from the onset to ensure a user-friendly product.
  2. Develop Prototypes Concurrently: While your machine learning team is building an ugly prototype to showcase a ready solution, engage your designer to develop wireframes of what the actual workflow could be. Use both prototypes for user interviews to validate your model and user experience.
  3. Team Cohesion is Essential: The success of your product hinges largely on the level of respect and cooperation among team members. Healthy relationships between product managers, designers, and engineers are crucial for delivering a good product.
  4. Does The Problem Really Need Machine Learning?: It is necessary to evaluate if the problem you are trying to solve necessitates machine learning. Regular expressions or open-source resources may provide simpler and more efficient solutions.
  5. Consider the Cost of Machine Learning Models: Data storage and transfer can be expensive. It is crucial to determine if customer demand justifies these costs before investing heavily in machine learning infrastructure.

Bonus Tip: Get Hands-On with Data

As a PM, involve yourself in data tagging to make informed decisions about classifiers, understand the data better, and tackle edge cases more pragmatically.

Leading Breakthroughs: A Reflection on Women in AI

The tech field is constantly evolving, and for women desirous of advancing their AI career, my advice is simple - be confident, ask questions, and drive conversations. This will greatly enhance your understanding of AI, make you a valuable team player, and propel your progress in the AI field.

By understanding the power of AI and the importance of team dynamics, you can successfully oversee innovative AI projects. Remember, curiosity and engagement are your most effective tools.


Video Transcription

Read More