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Keynote by Mike Gualtieri, Forrester Research - Making AI Happen Without Getting Fired - H2O World


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This session was recorded in San Francisco on February 5th, 2019 and can be viewed here:

AI is real. Enterprises use it to automate decisions, hyper-personalize customer experiences, streamline operational processes, and much more. However, for most enterprise technology leaders, AI technologies and use cases are still far too mysterious. The field is moving fast. Enterprise leaders must forge a coherent, pragmatic AI strategy that is tied to business outcomes. In this session, guest speaker Forrester Research Vice President & Principal Analyst Mike Gualtieri will demystify enterprise AI, identify use cases most likely to succeed, and, most importantly, provide key advice to enterprise leaders that are charged with moving AI forward in their organization.

Bio: Mike's research focuses on software technologies, platforms, and practices that enable technology professionals to deliver digital transformations that lead to prescient digital experiences and breakthrough operational efficiency. His key technology coverage areas are AI, machine learning, deep learning, AI chips and systems, digital decisions, streaming analytics, prescriptive analytics, big data analytical platforms and tools (Hadoop/Spark/Flink; translytical databases), optimization, and emerging technologies that make software faster and smarter. Mike is also a leading expert on the intersection of business strategy, artificial intelligence, and innovation. Mike provides technology vendors with actionable, fine-tuned advisory sessions on strategy, messaging, competitive analysis, buyer-persona analysis, market trends, and product road maps for the areas he directly covers and adjacent areas that wish to launch into new markets or use new technologies. Mike is a recipient of the Forrester Courage Award for making bold calls that inspire leaders and guide great business and technology decisions.

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Keynote by Mike Gualtieri, Forrester Research - Making AI Happen Without Getting Fired - H2O World

  1. 1. Making AI Happen Without Getting Fired Mike Gualtieri, VP & Principal Analyst 5 February 2019
  2. 2. AI is set to dominate enterprise innovation for many years to come.
  3. 3. 4© 2019 FORRESTER. REPRODUCTION PROHIBITED. “AI” implementation gains momentum Q.A13: What are your firm's plans to use the following analytics technologies? (Artificial intelligence) Base: 2094, 2106*, 1742 + data and analytics decision-makers Source: Forrester Data Global Business Technographics® Data And Analytics Survey, 2016, 2017, 2018 No to AI Yes to AI 6% 29% 25% 40% 7% 23% 20% 51% 8% 19% 20% 53% Don't know Not interested/no immediate plans Planning to implement within the next 12 months Implementing, implemented or expanding2016 2017* 2018+
  4. 4. 5 ADVICE FORRESTER Set proper expectations for AI by using the right definition.
  5. 5. Forrester recognizes two types of AI: Pure and Pragmatic.
  6. 6. Pure AI strives to imitate comprehensive human intelligence…
  7. 7. Pragmatic AI is narrower in scope, but often exceeds human intelligence.
  8. 8. 9© 2019 FORRESTER. REPRODUCTION PROHIBITED. Source: Forrester Research, Inc. Pragmatic AI Nope! Huge pre- requisite Yep!
  9. 9. 10© 2019 FORRESTER. REPRODUCTION PROHIBITED. Pragmatic AI is not one technology. It is comprised of one or more building block technologies. PhysicsDataHumans
  10. 10. Machine learning algorithms analyze data to models that make predictions, take decisions, or identify context.
  11. 11. Focus on pragmatic AI powered by machine learning.
  12. 12. I shall.
  13. 13. 14 ADVICE FORRESTER Choose more than one high-ROI use case.
  14. 14. There are as many use cases as there are business processes and customer experiences.
  15. 15. 16© 2019 FORRESTER. REPRODUCTION PROHIBITED. Enterprise decision makers agree that ML is applicable and important to all aspects of the business. Base: 353 global decision makers involved with machine learning and familiar with enterprise applications Q1-Please rate how much you agree or disagree with the following statements Hyper-personalization! Operational Efficiency! Competitiveness! Customer experience!
  16. 16. Predict supply-chain issues when there is still time to do something about it.
  17. 17. Prevent this dude from cyber attacking.
  18. 18. Prevent operations shutdowns by predicting machine failure before it happens.
  19. 19. Automate inspections to optimize deployment of field personnel.
  20. 20. Customize catering to reduce cost by 12%.
  21. 21. Find intelligence on potential investment opportunities.
  22. 22. Image source: iStockphoto Hyper-personalize customer experiences with targeted offers.
  23. 23. Deep learning stoked-up accuracy on images, voice, and natural language.
  24. 24. Diagnose disease more accurately and faster than doctors.
  25. 25. Automatically assess damage and repair costs.
  26. 26. Mitigate freight train slowdowns to save ~$70M per year.
  27. 27. Choose multiple AI projects like a VC chooses startups.
  28. 28. I shall.
  29. 29. 30 ADVICE FORRESTER Insist on comprehensive access to enterprise data.
  30. 30. Garbage In = Garbage Out
  31. 31. Organizations have dozens and hundreds of applications that generate valuable data.
  32. 32. 33© 2019 FORRESTER. REPRODUCTION PROHIBITED. Enterprise data is super rich - needed for successful, pervasive machine learning › Customer transaction data › Point-of-sale data › Customer and supplier contract data › Inventory data › Supply chain data › Product/service data › ERP and manufacturing data › Supplier transactions › R&D data › Sales and CRM data › Marketing/advertising data › Human resources data › Finance/accounting data
  33. 33. Algorithms get all the press, but it is the data that leads to success.
  34. 34. Understood.
  35. 35. 36 ADVICE FORRESTER Go faster with Auto-ML.
  36. 36. Data scientists aren’t expensive. They are inefficient.
  37. 37. 38© 2019 FORRESTER. REPRODUCTION PROHIBITED. The ML model building lifecycle is highly iterative and continuous. Model training using ML algorithms ML models Big data ingestion, processing and preparation Model scoring/inferencing in applications Retraining on new data Production Development
  38. 38. 39© 2019 FORRESTER. REPRODUCTION PROHIBITED. Machine learning solutions fall under three market segments Multimodal Notebook-based Widest breadth of workbench tools Code-first workbench for R, Python, etc. Automation- focused Designed specifically for automation
  39. 39. Auto-ML solutions dramatically compress the model building lifecycle: feature engineering, algorithm selection, evaluation, and tuning.
  40. 40. Data-savvy users can build machine learning models for many business-worthy use cases.
  41. 41. Add Auto-ML to your repertoire of machine learning model development tools.
  42. 42. Yessir.
  43. 43. 44 ADVICE FORRESTER Know when to quit.
  44. 44. Machine learning is not guaranteed to work…
  45. 45. …that’s why you identify more than one potential use case.
  46. 46. If the data doesn’t fit, you must quit.
  47. 47. Roger that big guy.
  48. 48. 49 ADVICE FORRESTER Keep production models fresh.
  49. 49. ML models are probabilistic.
  50. 50. 51© 2019 FORRESTER. REPRODUCTION PROHIBITED. ML model performance can decay over time Positive Minimum Business Value BusinessValue Time deployed Negative Maximum Business Value Model Model Model = probabilistic model
  51. 51. 52© 2019 FORRESTER. REPRODUCTION PROHIBITED. Dev-developed code always runs as written PositiveBusinessValue Time deployed Negative Code Code = deterministic code Code
  52. 52. 53© 2019 FORRESTER. REPRODUCTION PROHIBITED. ML models must be monitored, retrained, and often remodeled.BusinessValue Time deployed PositiveNegative Maximum Business ValueModel Model Model = probabilistic model Negative Business Value
  53. 53. Model operations is protection against undesirable results.
  54. 54. Model staging because the real evaluation is on production data. A B
  55. 55. Monitor monitoring to make sure the model is still performing as expected and not doing harm.
  56. 56. DevOps collaboration to have a repeatable model deployment process.
  57. 57. 58© 2019 FORRESTER. REPRODUCTION PROHIBITED. The MLOps Engineer role acts to usher models safely within the entire lifecycle MLOps Engineer DevOpsApp Dev Data Science App Design Data Engineer Business ModelOps ModelOps Ops
  58. 58. Integrate ModelOps with DevOps.
  59. 59. Aye, aye sir.
  60. 60. 61 ADVICE FORRESTER Get your business and IT ducks in a row.
  61. 61. A machine learning model is only successful if it is deployed.
  62. 62. ML models impact business process.
  63. 63. Ml models impact application design and development.
  64. 64. Engage with business and app dev early.
  65. 65. Easier said than done, but…ok.
  66. 66. 67 ADVICE FORRESTER You don’t have to do what the model tells you to do.
  67. 67. Govern rules using digital decisioning.
  68. 68. I shall.
  69. 69. A few models here and there are valuable and significant, but they are a mere drop in the bucket compared to what is possible.
  70. 70. 11001001101 0100100 010011001 010 Historical Transactions Customerdata Security Data infrastructure must meet the unique requirements of machine learning.
  71. 71. AI is the fastest growing workload on the planet earth.
  72. 72. Ten percent of firms leveraging AI will use digital decisioning to enhance and govern AI models. 1. Set proper expectations for AI. 2. Choose more than one use-case. 3. Insist on comprehensive data access. 4. Go faster with auto-ML. 5. Know when to quit. 6. Keep production models fresh. 7. Get business and IT engaged early. 8. Ignore the model to protect the