The AI/ML Advantage for Product Managers

List of courses and resources to learn AL/ML

Author

Sherman Jiang
January 27, 2024

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Empowering product managers with the tools and knowledge to create products of impact

The AI/ML Advantage for Product Managers

If you’re a Product Manager, it’s important to get familiar with AI and ML. Why? Because every Product Manager will be an AI Product Manager going forward.

  • Understand the technology of AI/ML as it changes the nature of our jobs

    • Know basic AI terminology and practices, such as Big Data, Machine Learning, Deep Learning, Neural Networks, Generative AI, and Computer Vision.

  • Improve your decision-making and productivity.

    • Learn how to be more strategic, plan and prioritize day-to-day work, and analyze product performance through Generative AI.

  • Enhance customer experience and business value.

    • Enable personalized offers and communications, measure consumer feedback and identify issues to address in real time.

  • Work effectively with ML engineers and data scientists.

    • Understand available approaches, such as machine learning, computer vision, natural language processing, and AI tools and algorithms.

    • Learn how to use Foundation Models to build intelligent applications faster.

If you’re looking for a place to start, here’s a list I put together. It’s by no means complete, but a good starting point. I’m happy to hear your thoughts and input to make the list better and up-to-date.

Foundation courses
Strategic insights

AI Strategy and Governance, University of Pennsylvania

Application in business

In payment and banking industry that I worked, there could be so many applications. just name a few:

  • Predictive analytics/trending: Pattern identification

  • Anomaly decision: Fraud detection and compliance, AML

  • Payment processing: Optimize Pricing, routing, reduce false positive in Authorization

  • Credit risk mitigation: Improve portfolio and collections management

You might look at applications in other areas, here are some places to start with:

AI Applications in Marketing and Finance University of Pennsylvania

Product management power-up

Advanced AI PM, Marily Nika

Ethics
More online courses

Prompt Engineering by Vanderbilt university

Mental models

Good books enable systemic learning and cut through noises.

A quick acid test

  1. which dataset you should use to train AI model and how to train the AI model?

  2. What is the differences between training data and test data?

  3. What are the supervised, unsupervised, reinforcement learning models?

  4. What is natural language processing (NLP), feedback loops?

If you are not sure about the answers, start happy learning now.

Answers

  1. Data and model determine your AI's brainpower. Think quality data as the fuel and targeted learning as the key to train a model! There are different models for different purposes e.g. classification, regression, generative models.

  2. Imagine training for an exam. Training data is your text book, practice tests, while test data reveals its real capabilities in exam.

  3. Think teachers, free exploration, and self-discovery. Each model learns differently, choose wisely!

  4. NLP allows your AI to chat like a pro, while feedback loops (positive or negative) refine its understanding. Think of it as continuous language coaching!

Author

Sherman Jiang, a product leader with a proven track record of success at Fortune 500 companies like Visa, HSBC, and Synchrony and honed expertise in Silicon Valley’s fast-paced tech scene. My passion lies in empowering payment and fintech companies through the power of Agile and AI augment. I specialize in engagement of team transformations, product strategy, product discovery, product development, and go-to-market execution. I’m also enthusiastic about how generative AI can make product managers better.