Applying a Serverless-First Mindset to bring AI into the Enterprise

Applying a Serverless-First Mindset to bring AI into the Enterprise

AI is becoming increasingly important to every business… but training everyone in your company to be a Data Scientist just isn't practical. Fortunately with AI becoming ever more democratized there are now ways of empowering more and more people within your organization. To do that, let's talk about:
- What a Serverless-First mindset is, and how we can apply it to AI
- What your Software Engineers really need to know about AI to use it
- What AI education non-technical members of the organization might need
- How we can take advantage of things like the AWS AI Stack to:
- Educate Software Engineers without prior AI
- Experience
- Do rapid prototyping and exploration
- Free up your Data Scientists to work on the most differentiating solutions
- Allow Software Engineers to effectively partner with Data Scientists

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Gillian Armstrong

August 17, 2020
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  1. Liberty IT Applying a Serverless-First Mindset to bring AI into

    the Enterprise Gillian Armstrong @virtualgill
  2. Liberty IT

  3. Liberty IT

  4. Liberty IT Gillian Armstrong @virtualgill Solutions Architect, AWS ML Hero

    Liberty IT, Belfast
  5. Gillian Armstrong // @virtualgill Serverless- First Mindset?

  6. Gillian Armstrong // @virtualgill An application that costs you nothing

    to run when no-one is using it (apart from data storage) Serverless Technology is…? Assembling your application using pay per use managed services
  7. Focusing on Functionality Offloading everything else Gillian Armstrong // @virtualgill

    A Serverless Mindset is…
  8. Focusing on Functionality Offloading everything else Gillian Armstrong // @virtualgill

    A Serverless Mindset is… Business Value
  9. Gillian Armstrong // @virtualgill Rent (Pay as you Go) Buy

    Build Software Ethos Only build what you need to / what will be a differentiator for you. Only continue until Value is Realized
  10. Gillian Armstrong // @virtualgill How does this apply to AI

    in the Enterprise…
  11. Data Scientists Software Engineers

  12. Gillian Armstrong // @virtualgill

  13. Gillian Armstrong // @virtualgill AWS ML Stack **other clouds are

    available
  14. Gillian Armstrong // @virtualgill AWS ML Stack Functions Databases Queues

    Logging Storage API Proxy
  15. Gillian Armstrong // @virtualgill AWS ML Stack

  16. Gillian Armstrong // @virtualgill - Rapid Delivery of Business Value

    and - Path to Machine Learning Upskilling across wider group of people Benefits of the AI Services…
  17. Gillian Armstrong // @virtualgill - Rapid Experimentation - Demonstrate Art

    of the Possible (especially in low-context / low data scenarios) - Less up-front need for specialists Rapid Delivery of Business Value
  18. Gillian Armstrong // @virtualgill - Low-Cost Prototypes using pay-as-you go

    services - Lower Maintenance / Management of the AI within your application Rapid Delivery of Business Value
  19. Gillian Armstrong // @virtualgill What did you learn? Path to

    Machine Learning Upskilling across wider group of people
  20. Gillian Armstrong // @virtualgill AWS ML Stack

  21. Gillian Armstrong // @virtualgill What did you learn?

  22. Gillian Armstrong // @virtualgill Why not start at the bottom?

    Developers love shiny stuff, they love to know how it works… source: am developer But we want to keep a Serverless Mindset, so a focus on Business Value first
  23. Employee Digital Assistant Gillian Armstrong // @virtualgill

  24. Gillian Armstrong // @virtualgill AWS Content Analysis Solution https://aws.amazon.com/solutions/implementations/aws-content-analysis/

  25. Gillian Armstrong // @virtualgill

  26. Gillian Armstrong // @virtualgill Deployment Tips… https://aws.amazon.com/solutions/implementations/aws-content-analysis/ - Resources are

    named based on the name you give the Stack so… - Don’t put ‘aws’ in the name (not allowed for some things like Cognito) - Keep it short or with the generated suffix you’ll go over the 63 chars allowed for an S3 bucket - Be aware some things carry a cost while they are running regardless of whether you are using it (circled)
  27. Gillian Armstrong // @virtualgill

  28. What did you learn? Possibilities of AI Gillian Armstrong //

    @virtualgill
  29. Gillian Armstrong // @virtualgill Amazon Comprehend Amazon Forecast Amazon Lex

    Amazon Personalize Amazon Polly Amazon Rekognition Amazon Textract Amazon Transcribe Amazon Translate Amazon Kendra Amazon Fraud Detector Amazon CodeGuru AWS AI Services
  30. What did you learn? Possibilities of AI Complexities of AI

    (expectation levelling) Gillian Armstrong // @virtualgill
  31. Gillian Armstrong // @virtualgill

  32. Gillian Armstrong // @virtualgill

  33. Gillian Armstrong // @virtualgill

  34. What did you learn? Possibilities of AI Complexities of AI

    (expectation levelling) Basic Concepts and Terminology Gillian Armstrong // @virtualgill
  35. What did you learn? Possibilities of AI Complexities of AI

    (expectation levelling) Basic Concepts and Terminology Mapping from Business Requirements/Question to Machine Learning Question Gillian Armstrong // @virtualgill
  36. Gillian Armstrong // @virtualgill

  37. What did you learn? Possibilities of AI Complexities of AI

    (expectation levelling) Basic Concepts and Terminology Mapping from Business Requirements/Question to Machine Learning Question Importance of Data! Gillian Armstrong // @virtualgill
  38. DATA • Quantity • Quality • Language / Linguistics •

    Feature Engineering Gillian Armstrong // @virtualgill
  39. What did you learn? Possibilities of AI Complexities of AI

    (expectation levelling) Basic Concepts and Terminology Mapping from Business Requirements/Question to Machine Learning Question Importance of Data! Applying Software Practices to AI Gillian Armstrong // @virtualgill
  40. AI SERVICES Gillian Armstrong // @virtualgill • Coding • Governance

    / Source Control • Deployment, CI/CD • Testing • Monitor
  41. ENGINEERING Gillian Armstrong // @virtualgill AI Everything else • Infrastructure

    as Code • Deployment, CI/CD • Testing • Security • Observability • Cost
  42. Gillian Armstrong // @virtualgill AWS Content Analysis Solution https://aws.amazon.com/solutions/implementations/aws-content-analysis/

  43. Gillian Armstrong // @virtualgill For this stack how many… Lambda

    Functions: Step Functions: 40 30
  44. Gillian Armstrong // @virtualgill

  45. Gillian Armstrong // @virtualgill For 2 photos and 1 video,

    how many… Lambda Functions Run: Step Function Transitions: 2,208 2,730
  46. ENGINEERING Gillian Armstrong // @virtualgill AI Everything else • Infrastructure

    as Code • Deployment, CI/CD • Testing • Security • Observability • Cost
  47. What did you learn? Possibilities of AI Complexities of AI

    (expectation levelling) Basic Concepts and Terminology Mapping from Business Requirements/Question to Machine Learning Question Importance of Data! Applying Software Practices to AI Legal, Security, Privacy and Ethics Gillian Armstrong // @virtualgill
  48. ETHICS Gillian Armstrong // @virtualgill • Humanizing Technology changes how

    we relate to it • Automation of Jobs • Privacy Concerns • Human Rights Concerns • Environmental Concerns
  49. ETHICS Gillian Armstrong // @virtualgill Always pair “What can we

    do”, with the question “What shouldn’t we do”
  50. What did you learn? Possibilities of AI Complexities of AI

    (expectation levelling) Basic Concepts and Terminology Mapping from Business Requirements/Question to Machine Learning Question Importance of Data! Applying Software Practices to AI Legal, Security, Privacy and Ethics Changing User Experience Gillian Armstrong // @virtualgill
  51. USER EXPERIENCE Gillian Armstrong // @virtualgill • Hey, there are

    still humans to think about too
  52. USER EXPERIENCE Gillian Armstrong // @virtualgill • Mental Models •

    Explainability and Trust • Feedback and Control
  53. Gillian Armstrong // @virtualgill https://pair.withgoogle.com/

  54. What did you learn? Possibilities of AI Complexities of AI

    (expectation levelling) Basic Concepts and Terminology Mapping from Business Requirements/Question to Machine Learning Question Importance of Data! Applying Software Practices to AI Legal, Privacy and Ethics Changing User Experience Gillian Armstrong // @virtualgill
  55. Gillian Armstrong // @virtualgill AWS ML Stack

  56. Gillian Armstrong // @virtualgill Rent (Pay as you Go) Buy

    Build AI Ethos Only build what you need to / what will be a differentiator for you. Only continue until Value is Realized.. Move forward based on - Functionality Limits Reached - Cost - Finding a Differentiator
  57. Gillian Armstrong // @virtualgill What did you learn? An Understanding

    of how to evaluate where AI could be used An Understand of how using AI changes your application A move towards a common understanding and collaboration with Data Scientists
  58. Gillian Armstrong // @virtualgill Data Scientists Software Engineers How Do

    I?
  59. Gillian Armstrong // @virtualgill Thank you! Get in touch! @virtualgill

  60. @Liberty Information Technology @Liberty IT @Liberty_IT