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AI Foundations Course Module 1 - Shifting to the Next Step in Your AI Transformation Journey

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In this session, you will learn about what you should do after you’ve taken an AI transformation baseline. Over the span of this session, we will discuss the next steps in moving toward AI readiness through alignment of talent and tools to drive successful adoption and continuous use within an organization.

To find additional videos on AI courses, earn badges, join the courses at H2O.ai Learning Center: https://training.h2o.ai/products/ai-foundations-course

To find the Youtube video about this presentation: https://youtu.be/K1Cl3x3rd8g

Speaker:
Chemere Davis (H2O.ai - Senior Data Scientist Training Specialist)

Published in: Technology
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AI Foundations Course Module 1 - Shifting to the Next Step in Your AI Transformation Journey

  1. 1. AI Foundations Module 1: An AI Transformation Journey Session 2: Shifting to the Next Step in the AI Transformation Journey
  2. 2. 2 What you can expect in this session 01 Introduction 02 Four Phases of AI Maturity 03 Success With AI 04 What’s Next?
  3. 3. 3 Module 1: An AI Transformation Journey Session 1: An AI Transformation Journey Primer (replay posted) Session 2: Shifting to the Next Step in the AI Transformation Journey Study Group Ask Me Anything Forum Session 3: AI Transformation and Covid-19 (Special Session) Module 2: Demystifying AI Module 3: Machine Learning Foundations AI Foundations Overview Interested in knowing the full schedule for the AI Foundations course? View the schedule on the community learning site You Are Here
  4. 4. Organizational AI Maturity Module 1: An AI Transformation Journey Session 2: Shifting to the Next Step in the AI Journey Part: 1
  5. 5. 5 AI is a Journey! For You and For Enterprises
  6. 6. 6 Who is on the team? Business leader, data scientists, IT professional Determine the problems you want to solve with metrics (time, money, # of customers, etc) Determine where you have data, need data, and can use technology to find answers and predictions. Find answers efficiently. Learn from others in the data science community Ask the Right Questions Data & Technology Community Create a Data Culture Understand and explain the models. Use leading edge technologies to guard for bias, explain a model, and present this to regulators Trust in AI 2 1 3 4 5 5 Keys to unlock AI
  7. 7. 7 AI Business Value a Journey in Four Phases 1 2 3 4Potential Operational Strategic Data-Driven Enterprise AI Journey Awareness & Interest Evaluate Business Value Technical Evaluation Point Deployment Point Production Enterprise Deployment Enterprise Production Modern AI Architecture Industry Leadership
  8. 8. Confidential8 Confidential8 Engagement Cycle (An Example) d 1. Data Assessment 2.Use Case Workshops With AI Experts 3.Accelerated Model Development via Automatic Machine Learning 4. Model Deployment 5. Measurement of KPI (Revenue Increase or Cost Decrease) Team should rapidly iterates through 5 key steps alongside stakeholders for each use case to focus on tangible, measurable results and turnkey work products
  9. 9. Confidential9 Strategy & Transformations Current-State Assessment Business Case Identification Technology & Organizational Review Strategic Vision & Innovation Responsible AI: Governance & Risks
  10. 10. Confidential10 AI Journey Example Value Time Partner Initial Use Case Drives Value AI Driven Business 0 (Baseline) Digital Transform & Data Management Initiatives Begin Basic Value From Organized/Structured Data Begins to Break Even Rapid Use Case Development Phase Multiple Use Cases Move into Production
  11. 11. Confidential11 AI Business Value a Journey in Four Phases - Roles 1 2 3 4Potential Operational Strategic Data-Driven AppsInfrastructureBusiness CXO, CDO, CIO, Line of Business Owner Business Analyst, Developers, Data Scientists, Data Workers, DBAs Data Architect, Dir. Of Security, Platform Operations, devOps, DBAs
  12. 12. Phase 1: Potential Module 1: An AI Transformation Journey Session 2: Shifting to the Next Step in the AI Journey Part: 2
  13. 13. Confidential13 Where are You? Maturity at Each Phase of the Journey 1 2 3 4Potential Operational Strategic Data-Driven AppsInfrastructureBusiness Focus on Entry Application Validate the Value of AI
  14. 14. Confidential14 Where are You? Maturity at Each Phase of the Journey 1 2 3 4Potential Operational Strategic Data-Driven AppsInfrastructureBusiness Data Scientist Data Engineer
  15. 15. Phase 2: Operational Module 1: An AI Transformation Journey Session 2: Shifting to the Next Step in the AI Journey Part: 3
  16. 16. Confidential16 Where are You? Maturity at Each Phase of the Journey 1 2 3 4Potential Operational Strategic Data-Driven AppsInfrastructureBusiness Focus on Entry Application Validate the Value of AI Deploy your next set of App(s) Choose Initial AI Advance Analytics App(s)
  17. 17. Confidential17 Where are You? Maturity at Each Phase of the Journey 1 2 3 4Potential Operational Strategic Data-Driven AppsInfrastructureBusiness Data Scientist Data Engineer Model Ops App Developer Business Decision Maker Project1 Data Scientist Data Engineer Model Ops ProjectN Business Decision Maker Module: 6
  18. 18. Phase 3: Strategic Module 1: An AI Transformation Journey Session 2: Shifting to the Next Step in the AI Journey Part: 4
  19. 19. Confidential19 Where are You? Maturity at Each Phase of the Journey 1 2 3 4Potential Operational Strategic Data-Driven AppsInfrastructureBusiness Focus on Entry Application Validate the Value of AI Deploy your next set of App(s) Choose Initial AI Advance Analytics App(s) Executive Sponsorship for organizational transformation Deploying Multi-tenant AI Platform Centralize AI Data Governance & Security
  20. 20. Confidential20 Organizational Hierarchy Refresher Centralized Decentralized Hybrid
  21. 21. Confidential21 Rich AI Ecosystem - Too Many Choices? Databases Big Data/Distributed Computing Cloud Computing Programming Languages Business Intelligence Data Science/Analytics/AI • Typical frontline store of data (relational, graph, etc) • May be hosted in cloud if volume of data warrants it • If data is too big to be useful for accessing it you can use big data platforms for distributed, parallel, high-performance computing • In terms of accessing, isolating, cleaning, transforming data, these are the big 3. • Python + R are consistently used for DS & modeling • Most common resources for descriptive statistics and dashboarding (specialize in descriptive stats) • For predictive & advanced analytic insights use Data Science/AI platforms (and py+R) to apply the highest quality methods. • Cloud computing may be needed to run heavy math for these models. Module: 5 ML Foundations ML Foundations
  22. 22. Confidential22 Where are You? Maturity at Each Phase of the Journey 1 2 3 4Potential Operational Strategic Data-Driven AppsInfrastructureBusiness Data Architect Business Unit1 Business Analysts Application Dev Lead Model Ops Data Engineer Enterprise Data Science Team BI Teams Divisional Governance Dir, IT Ops Application Dev Lead P1 P2 PN Business UnitN Business Analysts Application Dev Lead Model Ops Data Engineer Enterprise Data Science Team BI Teams Divisional Governance Dir, IT Ops Application Dev Lead P1 P2 PN
  23. 23. Confidential23 Confidential23 Two Sides of the Same Coin Understanding the entire scope of risks of deploying AI is imperative for success Risk For firms to fully adopt & take of AI, trust & buy-in critical Trust Correctly understanding & quantifying risk increases trust Knowing how & when to trust your AI decreases risk Business leaders are focused on decreasing risk, while data scientists are focused on understanding and trust of these models. Ultimately, these goals go hand in hand in hand.
  24. 24. Confidential24 Responsible AI Responsible AI Ethical AI Secure AI Explainable AI Interpretable Machine Learning Human-Centered Machine Learning Compliance RESPONSIBLE AI As the field has evolved, many definitions and concepts have come into the mainstream, below we outline H2O.ai’s respective definitions & understanding around the factors that make up Responsible Artificial Intelligence. • Explainable AI: Focuses on the ability analyze a ML model after it has been developed • Interpretable Machine Learning: Transparent model architectures and increasing how intuitive and understandable ML models can be • Ethical AI: Sociological fairness in machine learning predictions (i.e., whether one category of person is being weighted unequally) • Secure AI: Debugging and deploying ML models with similar counter-measures against insider and cyber threats as would be seen in traditional software • Human-Centered ML: User interactions with AI and ML systems
  25. 25. Confidential25 Confidential25 Data Loading, Prep, Exploratory Analysis, and Feature Engineering Model Training & Assessment Review for Explainability, Interpretability, and Fairness Deploy Model in Production Start Pass Pass Pass Responsible AI Process Overview Published by H2O.ai Ongoing Review and Monitoring Steps where data/statistical/human bias can enter the process Fail & Review
  26. 26. Phase 4: Data-Driven Module 1: An AI Transformation Journey Session 2: Shifting to the Next Step in the AI Journey Part: 5
  27. 27. Confidential27 Where are You? Maturity at Each Phase of the Journey 1 2 3 4Potential Operational Strategic Data-Driven AppsInfrastructureBusiness Focus on Entry Application Validate the Value of AI Deploy your next set of App(s) Choose Initial AI Advance Analytics App(s) Executive Sponsorship for organizational transformation Deploying Multi-tenant AI Platform Centralize AI Data Governance & Security Insights from Data becomes a Competitive Advantage Widespread Adoption & Consumption Continuously Refine AI Best Practices
  28. 28. Confidential28 Where are You? Maturity at Each Phase of the Journey 1 2 3 4Potential Operational Strategic Data-Driven AppsInfrastructureBusiness AI Architect Model Ops Data Workers Data Science Team Corporate Governance Dir, IT Ops Application Dev Lead P1 P2 CDO BU1 BU1 Business Analysts BI Teams Business Analysts BI Teams P1 P2 P1 P2P1 P2 CTO Application Architect End User Service Desk Infrastructure Administration Change & Program Mgt.
  29. 29. Confidential29 Where are You? Maturity at Each Phase of the Journey 1 2 3 4Potential Operational Strategic Data-Driven Data or Capital? Market Capitalization Peer Group Leaders A Mature Data Driven Organization treats Data like Capital Chief Financial Officer Managed for shareholder value Top-down mandate to allocate capital Cost control pushed to front-line Cost of capital Every dollar can be invested once Value compounds over time Chief Data Officer Managed for shareholder value Top-down mandate for a data-driven organization Data-driven decision making pushed to front line Cost of data processing infrastructure and expertise Data is an organizational asset
  30. 30. Organize for Success With AI Module 1: An AI Transformation Journey Session 2: Shifting to the Next Step in the AI Journey Part: 6
  31. 31. Confidential31 Organizing your AI Journey for Success Enable self-service for the business • Analysts, data scientist and developers aligned with the particular needs of each business • AI consumable for different use cases/workloads • Multiple lenses on the same data, with team specific views Central operational constructs • Better ability to attract, retain and develop specialist staff • Consistent data science best practice enforcement and compliance • Optimal capital efficiency driven by scale and a multi-tenant shared service • Modular, enterprise architecture that supports the broadest range of applications and analyses
  32. 32. Confidential32 What We’ve Covered So Far... • AI Transformation requires a journey through the phases of maturity: – Potential – Operational – Strategic – Data-Driven • Embracing the 5 Keys to Unlocking AI play a crucial role in moving to maturity • As maturity increases, data becomes an organizational asset that can be turned into real business value • AI adoption improves when there is clear strategy and alignment with talent, technology, and goals. Recording will be posted w/in 2 days
  33. 33. What’s Next Module 1: An AI Transformation Journey Session 2: Shifting to the Next Step in the AI Journey Part: 7
  34. 34. Confidential34 1. Study Group on Saturday July 4, 2020 @ 7:00AM PDT 2. Ask Me Anything on Sunday July 5, 2020 @ 7:00AM PDT 3. The next session AI Transformation & Covid-19 will be held on Monday July 6, 2020 @ 7:00AM PDT 4. Module 2: Demystifying AI will begin on Tuesday July 7, 2020 @ 7:00AM PDT. Upcoming Sessions
  35. 35. Confidential35 Confidential35 Quizzes & Study Groups • Each session within a module will have a small quiz to complete and all quizzes for that module will be due before the next module starts. • There are 2 options available for you to ask additional questions or get assistance on AI concepts covered in the sessions: – A Study Group for each Module will be held on Saturdays @ 7:00AM PDT – Ask Me Anything will be held on Sundays @7:00AM PDT • Reminder: Don’t forget to complete Quiz 2: Shifting to the Next Step in the AI Transformation Journey by Tuesday July 7, 2020 to earn your badge!
  36. 36. Resources
  37. 37. Confidential37 Confidential37 Additional Resources H2O.ai’s AI Glossary H2O.ai Webinar - Maximizing the Impact of Machine Learning through Model Ops H2O.ai Webinar - Key Terms and Ideas in Responsible AI H2O.ai Webinar - Your AI Transformation

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