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The talk focused on the Fundamentals of Product Management, leveraging the speaker's personal experiences in the AI field. It covered core Product Manager topics such as managing customer needs, business goals & technology feasibility, the holy trinity of the Product Manager discipline, delve into data analyses, rapid experimentation, and execution, and finally, explored the challenges of customer privacy, bias, and inclusivity in AI products.
▪ More than a decade as a MSFT
▪ Drive AI strategy for customer engagement & support
▪ Involved in recruiting and developing PM talent
▪ Love travelling; dabbling in music & wildlife photography
Structure of Today’s talk
• Introduction to Machine Learning and AI
• Building AI products
• Data and Experimentation
• The Social Sciences of AI
AI refers to tasks that are quintessentially
• AI research started in the 50s
• 2012: The inflection point (AlexNet)
• Deep Learning becomes
mainstream helped by compute,
data, & models
• Exponential growth in the last 5
If the last decade belonged to the app economy, the next could be in AI!
A Machine Learning Model
• Takes a set of inputs
• Performs some operations
• Using set of parameters (weights)
• To provide some outputs
Training a model
• Process of optimizing parameters (weights)
• Parameters tweaked using Gradient
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• Has some basic operations on inputs
• And a non linearity (e.g. ReLu Function)
• Each output of Neuron is fed forward to the next
• Called as a Layer of the neural network
• Connect layers (feed outputs to inputs)
• Same or different number of neurons per layer
• Also called as a Fully Connected Network
• Neural Nets are trained using Back
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• Wow that’s super deep!
• Millions of parameters
• Layers can combine in any number of
• Require massive compute and data to train
• Can perform highly complex tasks
Deep Learning techniques
• CNN + RNN (“Space-Time” e.g. scene
• General Adversarial Networks (e.g. Deep Fake)
• Transfer Learning
• Reinforcement & Online Learning
Worthwhile to read about these and take some courses!
• Convolutional Neural Networks (“Spatial”, e.g.
• Recurrent Neural Networks (“Temporal”, e.g. speech)
Products that are viable and valuable
• Are rooted in fundamentals
• Solves a real customer need
• Provides business value
• Technically feasible
• Have a sound long term Strategy
• Has competitive advantages
• Well positioned and aligned with ecosystem
• Has growth potential
• Well executed
• Decisions, tradeoffs, launch
PMs drive the vision & strategy … with a lot of influencing!
It starts with Customers
• Establish your North Star
• Build compelling user stories and scenarios
• All products have customers. Develop a deep understanding of them
Customer need Magic Happens Happy Customer
PMs put customers first!
From North Star to your next steps
Cost & Complexity
No Brainers (1) Strategic Initiatives (2)
Low hanging fruits (3) Not worth it (4)
• Use framework to establish MVP
• Market research, Kano study, competition
• Cost/complexity can be from technology,
dependencies, resources, or timelines
• Appropriately balance feature v/s
PMs drive prioritization!
Building out your solution
• Start simple. Focus on a small set of core user scenarios. Do them well!
• Build a reasonable experience without AI
• For AI, ask how a normal person would do it?
PMs execute – who, what, when, how!
• Iterate, learn, improve
Applying AI to your user scenarios
• Are there patterns?
• Would an expert be able to predict
• Do we have enough data needed for
• Do I really need deep learning?
Complexity of model
PMs are pragmatic about the possibilities & limitations of technology!
• Often OK to start with rules based approaches
• Use available AI frameworks for prototyping
Can’t manage what you can’t measure…
• KPIs, KPIs, KPIs
• Based on customer and business goals
• Simple, measurable, sensitive
• Do you have baselines?
• What does success look like?
• What tradeoffs are you willing to make?
PMs live and breathe KPIs!
• One small change at a time
• Reasonable hypothesis
• Success metrics, guardrails, & tradeoffs
• Well defined control and treatment groups
• Isolated, randomized, & equal samples
• Statistically significant outcomes
• Avoid gaming
Build a “data oriented” culture in your team!
• Face ID (security)
• High Threshold
• Pros: Blocks all imposters
• Cons: You get denied often
• Low Threshold
• Pros: You never get denied
• Cons: Lets in imposters
• Formally: Precision and Recall
• or Specificity and Sensitivity
• Precision = [TP] /
True Positive False Positive
False Negative True Negative
• Recall = [TP] / [TP+FN]
• Often analyzed using ROC curves
• Area under the curve implies model quality
PMs own the product decisions!
• Cost of irrelevant v/s the cost of missing relevant results
• Hard to change recall, but precision can be improved
• 2 Factor Authentication, 2nd independent test
• Additional information from user
• Optimal point determined by business requirements
I think therefore I am …
Bias in AI
• Bias in user stories
• Bias in training data
• Bias in outcomes
Awareness is a great first step!
• How human should your product be?
• Should it have a personality?
• Socio economic impact
Part-time Product Management Courses in
San Francisco, Silicon Valley, Los Angeles, New York, Austin,
Boston, Seattle, Chicago, Denver, London, Toronto