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AI Product Manager

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Presentation "AI Product Manager" at the Digital Product School (on 10/22/2020) from Datentreiber.

Content:

• Overview over the AI product innovation cycle
• AI Thinking: ideating and prioritizing the right use cases
• AI Prototyping: testing critical hypotheses with experiments
• AI Engineering: building scalable & user friendly AI applications
• AI Management: maintaining AI solutions with DataOps
• Outlook: how to become an AI product manager (links & more)



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AI Product Manager

  1. 1. Martin Szugat @ Digital Product School on 10/22/2020 AI Product Manager
  2. 2. Agenda Overview over the AI product innovation cycle00:15 AI Thinking: ideating and prioritizing the right use cases00:30 AI Prototyping: testing critical hypotheses with experiments00:45 AI Engineering: building scalable & user friendly AI applications01:00 AI Management: maintaining AI solutions with DataOps01:15 Outlook: how to become an AI product manager (links & more)01:30 Q&A01:45 Welcoming: introduction & agenda00:00
  3. 3. 4 1996-2008 IT-Consultant, Author and Software Developer Study and Research of Bioinformatics (Data Science) 2001-2008 Managing Director & Shareholder of SnipClip GmbH (Marketing Agency) 2008-2013 Program Director of the Predictive Analytics World & Deep Learning World (Conference Series) 2014-dato Managing Director & Founder of Datentreiber GmbH (Consultancy) 2014-dato Advisory Board for Media & IT for DDG AG (AI Company Builder) 2020-dato Martin Szugat Shareholder of Digitaltreiber GmbH (Recruitment Agency) 2016-dato Chief Data Officer & Shareholder of 42AI GmbH (AI Market Network) 2018-dato
  4. 4. 5
  5. 5. Agenda Welcoming: introduction & agenda00:00 Overview over the AI product innovation cycle00:15 AI Thinking: ideating and prioritizing the right use cases00:30 AI Prototyping: testing critical hypotheses with experiments00:45 AI Engineering: building scalable & user friendly AI applications01:00 AI Management: maintaining AI solutions with DataOps01:15 Outlook: how to become an AI product manager (links & more)01:30 Q&A01:45
  6. 6. Agenda Welcoming: introduction & agenda00:00 AI Thinking: ideating and prioritizing the right use cases00:30 AI Prototyping: testing critical hypotheses with experiments00:45 AI Engineering: building scalable & user friendly AI applications01:00 AI Management: maintaining AI solutions with DataOps01:15 Outlook: how to become an AI product manager (links & more)01:30 Q&A01:45 Overview over the AI product innovation cycle00:15
  7. 7. 9 AI Product Machine LearningData x = What is an AI product?
  8. 8. 10 Data ProductAnalyticsData x = What is a data product?
  9. 9. What is an AI (Data + Analytics) Strategy? Accessible Data AI Use Cases Company’s Objectives Data / AI Products with a Business Case
  10. 10. Collection of Analytics Use Cases (Problem, Solution, Benefit) Roadmap for Data-Driven Business Cases (Costs, Risks, Profits) Assumptions (Analytical, Economical, …) Learnings (Data, Business, User, …) Business Value (Information → Decision → Action → Impact → Objective) Data Sources (Collection, Acquisition, …) Data Thinking Data Mining Data Engineering Data Management Data Strategy Data Prototypes Data Product Data SourcesData AssetsData Product Innovation Cycle
  11. 11. Data Management Data Engineering Data Mining Data Thinking Data, Model & Product Management Data, Software & UI Engineering Data Mining & User Experiments Data & Design Thinking 2. User Under- standing (Desirability) 3. Data Under- standing (Feasibility) 1. Business Under- standing (Viability) 2. Modelling & Visualization 3. Evaluation 1. Data Exploration & Preparation 3. Learn 1. Build 2. Measure 3. Monitor 1. Deploy 2. Orchestrate CRISP-DM Design Thinking Proof of Concept (PoC)? Proof of Value (PoV)? Lean Develop- ment DataOps
  12. 12. Data, Model & Product Management Operating Data, Software & UI Engineering Engineering Data & Design Thinking Data Mining & User Experiments Designing Experiment-ing Data LabData Factory ➔ Exploration to Learn ➔ Exploitation to Earn
  13. 13. 15 Designing Experiment-ingEngineering Operating Data Strategist, AI Translator, … Canvas, Mockups, … Design Thinking, Sprints … Data Scientist, UX Designer, … Data Analytics, Modelling, … CRISP-DM, Kanban, … Data Steward, Product Manager, … Monitoring, Audits, … DataOps, SPC, … Data Engineer, Developer, … Cloud, MapReduce, … Scrum, Lean … Skills, Tools & Methods
  14. 14. 16 Designing Experiment-ingEngineering Operating Data Strategist, AI Translator, … Canvas, Mockups, … Design Thinking, Sprints … Data Scientist, UX Designer, … Data Analytics, Modelling, … CRISP-DM, Kanban, … Data Steward, Product Manager, … Monitoring, Audits, … DataOps, SPC, … Data Engineer, Developer, … Cloud, MapReduce, … Scrum, Lean … AI Product Manager
  15. 15. Agenda Welcoming: introduction & agenda00:00 Overview over the AI product innovation cycle00:15 AI Prototyping: testing critical hypotheses with experiments00:45 AI Engineering: building scalable & user friendly AI applications01:00 AI Management: maintaining AI solutions with DataOps01:15 Outlook: how to become an AI product manager (links & more)01:30 Q&A01:45 AI Thinking: ideating and prioritizing the right use cases00:30
  16. 16. Holy Grail Use Cases Everybody wants it. Nobody has it. Some claim to have it.
  17. 17. Moonshots 4% of the US state budget was invested in the Apollo program.
  18. 18. Lighthouse Use Cases If you head for the lighthouse, you'll probably shipwreck.
  19. 19. Pet Projects Bosses are usually furthest away from the actions and thus the relevant information.
  20. 20. Boring is the new Sexy. Look for use cases that sound boring because they often are very subject-specific.
  21. 21. Delegate Form Check Stake- holders on board Business Plan Check Proof of Concept (PoC) Integration Tests Proof of Value (PoV) Ideas Use Cases (Drafts) Business Cases (Concepts) Prototypes Releases MVDP * Meet-ing Work- shop Designing Experimenting Engineering Operating Use Case Ideation & Prioritization Process Count Effort ? Backlog * MVP: Minimum Viable (Data) Product Data (Product Design) Sprints (Agile) Develop-ment Sprints
  22. 22. From Use Cases to Business Cases User Under- standing Business Under- standing Data Under- standing Users Problems Solutions Benefits ? Use Cases Costs Risks Profits Business Cases ? Object- ives Results Actions Decisions ? Diverge Converge Diverge Converge Diverge Converge Viability Desirability Feasibility
  23. 23. 1st Day: Overview of Actual Status & Outlook on Target Status. 2nd Day: In-depth Look & Check into the Details.
  24. 24. Martin Szugat & Martijn Baker @ Data Brain Meetup: ➔ https://www.slideshare.net/Datentreiber/presentations ➔ https://www.youtube.com/watch?v=U8EbR2gnl_o Data Strategy Design: An Open Source Toolbox & Method for Data Thinking
  25. 25. Agenda Welcoming: introduction & agenda00:00 Overview over the AI product innovation cycle00:15 AI Thinking: ideating and prioritizing the right use cases00:30 AI Engineering: building scalable & user friendly AI applications01:00 AI Management: maintaining AI solutions with DataOps01:15 Outlook: how to become an AI product manager (links & more)01:30 Q&A01:45 AI Prototyping: testing critical hypotheses with experiments00:45
  26. 26. Cross Industry Standard Process for Data Mining https://en.wikipedia.org/wiki/Cross_Industry_Sta ndard_Process_for_Data_Mining LEARN BUILD MEASURE
  27. 27. Hypotheses-Driven Innovation Build Experi- ments Measure Metrics Learn from Obser- vations Hypotheses(In-)Validated Viability Feasi- bility Desir- ability
  28. 28. Assum- ption
  29. 29. Assum- ption A demand forecast of accuracy x% will decrease out of stock situations by y% and thus save the company z% euros per year. 12/2020 Martin Szugat 2 Month Build a simple machine learning model and test it with n users (demand planners). Model performance as RMSE as well as business performance as OoS delta rate. Prediction Performance RMSE < e.g. current estimation OoS rate > -10% → Saved costs per year = 1M € ➔ Positive estimated ROI for project
  30. 30. Assum- ption
  31. 31. Assum- ption
  32. 32. Assum- ption
  33. 33. Assum- ption Business performance doesn’t scale with model performance 10.12.2020 Martin Szugat A better demand forecast prediction will reduce out of stock situations. That even if the RSME is improved by 10% the OoS rate is only decreased by 2%. Model performance and business performance doesn’t scale the same level. Test other machine learning approaches to improve RSME by x%.
  34. 34. Assum- ption
  35. 35. Assum- ption
  36. 36. Assum- ption
  37. 37. Assum- ption Canvas Research Prototype Pilot … Product Explo-ration
  38. 38. Hypothesis, Experiments & Learnings Database (HELD): ➔ https://dtbr.de/held
  39. 39. Agenda Welcoming: introduction & agenda00:00 Overview over the AI product innovation cycle00:15 AI Thinking: ideating and prioritizing the right use cases00:30 AI Prototyping: testing critical hypotheses with experiments00:45 AI Management: maintaining AI solutions with DataOps01:15 Outlook: how to become an AI product manager (links & more)01:30 Q&A01:45 AI Engineering: building scalable & user friendly AI applications01:00
  40. 40. PoC Trap • Data & technology faith • “Throwing over the fences” phenomena • “Not thought through to the end” mindset
  41. 41. Value Pipeline Business Value Analytics in Production Data Sources PoC Concept Idea InnovationPipeline Exploration vs. Exploitation / Learn vs. Earn Operation: Maintain Innovation: Change Clash of Interests & Culture!
  42. 42. Titel Text Zoom in
  43. 43. Experiment vs. Test Source: https://medium.com/data-ops/dataops-is-not-just-devops-for-data-6e03083157b7 Test Experiment
  44. 44. 48 Exploration Stage Gold Standard Data Sets Analytics in Production Data Lakeland Validation Stage: Real World Data Sets Production Stage “Real Time” Data Sets Moni- toring Analytics in Development Analytics in Experimentation Frequent Exports Sporadic Exports Sandboxes
  45. 45. 49 Testing. Testing. Testing. Source: https://martinfowler.com/articles/cd4ml.html
  46. 46. 50 Continuous Deployment & Integration Source: https://martinfowler.com/articles/cd4ml.html
  47. 47. Agenda Welcoming: introduction & agenda00:00 Overview over the AI product innovation cycle00:15 AI Thinking: ideating and prioritizing the right use cases00:30 AI Prototyping: testing critical hypotheses with experiments00:45 AI Engineering: building scalable & user friendly AI applications01:00 Outlook: how to become an AI product manager (links & more)01:30 Q&A01:45 AI Management: maintaining AI solutions with DataOps01:15
  48. 48. 52 Throwing over the fences.
  49. 49. Designing Experiment-ingEngineering Operating Innovation Lab Data LabData Factory IT
  50. 50. You build it. You fix it.
  51. 51. Designing Experiment-ingEngineering Operating DataOps You design it. You test it. You build it. You fix it. Data Strategists Data Scientists Data Engineers Data Stewards
  52. 52. 56 DataOps is NOT Just DevOps for Data Source: https://medium.com/data-ops/dataops-is-not-just-devops-for-data-6e03083157b7
  53. 53. Eat your own dog food: analytics for analytics.
  54. 54. Source: https://www.slideshare.net/PAWDeutschland/data-science-development-lifecycle-everyone-talks-about-it-nobody-really-knows-how-to-doit-and-everyone-else-is-doing-it
  55. 55. Agenda Welcoming: introduction & agenda00:00 Overview over the AI product innovation cycle00:15 AI Thinking: ideating and prioritizing the right use cases00:30 AI Prototyping: testing critical hypotheses with experiments00:45 AI Engineering: building scalable & user friendly AI applications01:00 AI Management: maintaining AI solutions with DataOps01:15 Q&A01:45 Outlook: how to become an AI product manager (links & more)01:30
  56. 56. Non-Linear Data Product Innovation Process (Cycle of Cycles) Designing Engineering Operating 5Prototypes 20Concepts 3Products 100Ideas 1System Experiment- ing Unit Data Strategy Data Lab Data Factory Data Operations Back to Backlog Back to Backlog PoC PoV Tests AI Product Manager
  57. 57. Raw Data Clean Data Value Pipeline Anomaly Detection PoC Concept Idea InnovationPipeline XYZ Prediction PoC Concept Idea InnovationPipeline Business Value Data & Analytics Pipelines
  58. 58. From Data Craftsmanship …
  59. 59. … to a Data Industry.
  60. 60. AI Product Manager Analytical Technical Business Design Thinking Product Design & Management DataOps Scrum / Kanban Data & Software Architecture Data Management & Governance Machine Learning Statistics CRISP-DM AI Governance Business Analyses Data Visualization & Storytelling Soft Skills: Moderation, Mediation, Negotiation, .. CI / CD DevOps UI / UX Lean Management
  61. 61. 65 Further literature 1. Data Strategy & Data Thinking 1. Design thinking for data products 2. Data Strategy: Good Data vs. Bad Data 3. How to Define and Execute Your Data and AI Strategy 4. See next slide 2. Data Science Development Process: 1. Data Science at Roche: From Exploration to Productionization 2. Data Science Development Lifecycle 3. DataOps / ModelOps / AIOps 1. DataOps is NOT Just DevOps for Data 2. The DataOps Cookbook 3. Introducing ModelOps To Operationalize AI 4. Monitoring Machine Learning Models in Production 5. Continuous Delivery for Machine Learning 4. AI Product Management 1. A step-by-step guide to becoming a Data Product Manager 2. Managing Data Science as Products 3. What you need to know about product management for AI 4. Practical Skills for The AI Product Manager 5. Bringing an AI Product to Market 5. Other 1. The New Business of AI (and How It’s Different From Traditional Software) 2. When is AI not AI?
  62. 62. Get started. • Designkit: http://dtbr.de/designkit • LinkedIn Group: http://dtbr.de/data-thinker • Video training: http://dtbr.de/ddm • Interactive trainings: http://dtbr.de/training • News: http://dtbr.de/twitter • Presentations: http://dtbr.de/slideshare • More: https://www.datentreiber.de
  63. 63. Source: https://medium.com/womeninai/can- artificial-intelligence-solve-my-business- problem-4ff3bcbffe32
  64. 64. Agenda Welcoming: introduction & agenda00:00 Overview over the AI product innovation cycle00:15 AI Thinking: ideating and prioritizing the right use cases00:30 AI Prototyping: testing critical hypotheses with experiments00:45 AI Engineering: building scalable & user friendly AI applications01:00 AI Management: maintaining AI solutions with DataOps01:15 Outlook: how to become an AI product manager (links & more)01:30 Q&A01:45
  65. 65. datentreiber.deWir treiben Ihr Unternehmen voran. Web: www.datentreiber.de Blog: www.datentreiber.de/blog/ Martin Szugat Geschäftsführer Telefon: +49 [0]881 12 88 46 53 Email: ms@datentreiber.de

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