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How to Use Artificial Intelligence by Microsoft Product Manager

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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.

Publié dans : Technologie
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How to Use Artificial Intelligence by Microsoft Product Manager

  1. 1. How to Use Artificial Intelligence by Microsoft Product Manager www.productschool.com
  2. 2. FREE INVITE Join 23,000+ Product Managers on
  3. 3. COURSES Product Management Learn the skills you need to land a product manager job
  4. 4. COURSES Coding for Managers Build a website and gain the technical knowledge to lead software engineers
  5. 5. COURSES Data Analytics for Managers Learn the skills to understand web analytics, SQL and machine learning concepts
  6. 6. COURSES Blockchain and Cryptocurrencies Learn how to trade cryptocurrencies and build products using the blockchain
  7. 7. TONIGHT’S SPEAKER Bodhi Deb
  8. 8. Managing AI Products
  9. 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. 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
  11. 11. Introduction to ML and AI
  12. 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. 13. A Machine Learning Model • Takes a set of inputs • Performs some operations • Using set of parameters (weights) • To provide some outputs I n p u t s O u t p u t s Σ Parameters Model
  14. 14. Training a model • Process of optimizing parameters (weights) • Parameters tweaked using Gradient Descent I n p u t s O u t p u t s Σ Parameters Check Performance Model Parameters* 11 22 Parameters** NN Final Parameters Tweak parameters
  15. 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. 16. Deep Learning … … … … … … … … … … … … … … … … … … … … … … … … … … … • 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. 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)
  18. 18. Building out AI Products
  19. 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. 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. 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. 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. 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
  24. 24. Data and Experimentation
  25. 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. 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. 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. 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
  29. 29. Social sciences of AI
  30. 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. 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