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.
9. About me
▪ More than a decade as a MSFT
PM
▪ Drive AI strategy for customer engagement & support
▪ Involved in recruiting and developing PM talent
▪ Love travelling; dabbling in music & wildlife photography
10. Structure of Today’s talk
• Introduction to Machine Learning and AI
• Building AI products
• Data and Experimentation
• The Social Sciences of AI
• Q&A
12. AI refers to tasks that are quintessentially
human
• Speech
• Language
• Vision
• Reasoning
• Perception
• 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
years
If the last decade belonged to the app economy, the next could be in AI!
13. A Machine Learning Model
• Takes a set of inputs
• Performs some operations
• Using set of parameters (weights)
• To provide some outputs
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Parameters
Model
14. Training a model
• Process of optimizing parameters (weights)
• Parameters tweaked using Gradient
Descent
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Parameters
Check
Performance
Model
Parameters*
11 22
Parameters**
NN
Final Parameters
Tweak
parameters
15. Neural Nets
Neuron
Feed Forward
Network
Neural Net
… … …
…
…
• 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
Propagation
16. Deep Learning
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• Wow that’s super deep!
• Millions of parameters
• Layers can combine in any number of
ways
• Require massive compute and data to train
• Can perform highly complex tasks
17. Deep Learning techniques
• CNN + RNN (“Space-Time” e.g. scene
description)
• 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.
vision)
• Recurrent Neural Networks (“Temporal”, e.g. speech)
19. 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
Customer
Benefits
Business
Objectives
Technology
PMs drive the vision & strategy … with a lot of influencing!
20. 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!
21. From North Star to your next steps
Customer
Value
Cost & Complexity
HL
H
L
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
engineering investments
PMs drive prioritization!
22. 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!
• Repeat
• Iterate, learn, improve
23. Applying AI to your user scenarios
• Are there patterns?
• Would an expert be able to predict
outcomes?
• Do we have enough data needed for
training?
• Do I really need deep learning?
Complexity of
prediction
Complexity of model
HL
H
L
Rules
Base
d
Descriptive
Statistics
Statistical
Inference
Naive Bayes
Classifiers
Markov
Process
Regression
Analyses
Monte Carlo
Methods
Linear
classifiers
Clustering
Instance-based
learning
Decision
Trees
Ensemble
Learning
Deep Learning
(CNN, RNN,
GANs)
Transfer
Learning
Reinforcement
Learning
PMs are pragmatic about the possibilities & limitations of technology!
• Often OK to start with rules based approaches
• Use available AI frameworks for prototyping
25. 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!
26. Experiments
• 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!
27. Business Decisions
• 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] /
[TP+FP]
True Positive False Positive
False Negative True Negative
FT
T
F
Predicted
Actual
• Recall = [TP] / [TP+FN]
28. Precision/Recall tradeoff
• 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
30. 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!
Quintessentially human
• How human should your product be?
• Should it have a personality?
• Socio economic impact
31. Part-time Product Management Courses in
San Francisco, Silicon Valley, Los Angeles, New York, Austin,
Boston, Seattle, Chicago, Denver, London, Toronto
www.productschool.com