ML & Chatbots workshop

Transcript

1. Nederlandse Spoorwegen
   Machine Learning Workshop
   Tessa Reef
   Lee Boonstra
   8 July 2019
   

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2. ▶ 11.00 - 11.30 Arrival/coffee & Intro by Tessa
   ▶ 11.30 - 12.30 Chatbots & Google Assistant by Lee
   ▶ 12.30 - 13.00 Lunch
   ▶ 13.00 - 14.00 Cloud AI by Lee
   ▶ 14.00 - 14.15 Quiz - (Win a Google Home!)
   ▶ 14.15 - 14.30 Conclusions by Tessa
   Agenda
   

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3. Lee Boonstra
   @ Google
   Customer Engineer, Google Cloud (2,5
   years)
   Dialogflow Expert
   Chapter Lead ERG Gayglers
   Public Speaker (since 2013)
   Writer/Blogger for Techzine, .Net
   Magazine, Marketingfacts.nl,
   CustomerTalk.nl and Google Cloud Blog
   www.leeboonstra.com
   

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4. Lee Boonstra
   before Google
   Book Writer O’Reilly
   (Hands-on Sencha Touch 2, mobile web development)
   Technical Trainer @ Sencha Inc.
   2012 - 2016
   Lead Client-side Engineer @ Valtech
   2009 - 2012
   Senior Java Developer @ Accenture
   2007 - 2009
   Founder of my own company
   2004
   www.leeboonstra.com
   

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5. Improve your customer care
   by building an AI platform with the use of Google Cloud
   Lee Boonstra
   Sales engineer Google Cloud
   

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6. Chatbots are expected to trim
   business cost by more than
   $8 billion per year by 2022
   Juniper Research
   

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7. Chatbots exists since the 90’s…
   So why are they popular now?
   

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8. FIRST CHATBOT, 1994
   

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9. SHIFT FROM A MOBILE FIRST
   TO AN AI FIRST STRATEGY
   

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10. HOW DID YOU LEARN YOUR FIRST LANGUAGE?
    

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11. 11
    Chatbots is all about
    Machine Learning!
    ● Natural Language Understanding
    ● Intent Matching
    ● Speech to Text
    ● Text to Speech (Wavenet)
    

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12. How to create chatbots?
    

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14. GOOGLE CLOUD HAS OVER 100 BUILDING BLOCKS
    INSTEAD LET’S FOCUS ON SOLUTIONS
    

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15. Introducing Cloud AI
    Less ML
    expertise
    More ML
    expertise
    Cloud AI solutions
    Cloud
    Job Discovery
    Contact
    Center
    Document
    Understanding
    Cloud AI building blocks
    Cloud AI platform
    Cloud ML
    Engine
    Cloud Video
    Intelligence
    ML professionals & service partners
    ASL
    Professional Services
    Organization
    Cloud
    AutoML Vision
    Vision
    Cloud
    Vision
    Language
    Cloud Natural
    Language
    Cloud
    AutoML NL
    Dialogflow
    Enterprise
    Cloud
    Translation
    Cloud
    Speech-to-Text
    Cloud
    Text-to-Speech
    Cloud AutoML
    Translation
    New
    New
    New
    Cloud
    GPU
    Cloud
    TPU
    Cloud
    Dataflow
    Cloud
    Dataproc
    Machine & Deep
    Learning
    ML accelerators ML libraries
    Tensorflow Kubeflow
    Kaggle/datasets
    Datasets
    

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16. 16
    ● Previously known as API.AI
    ○ (Sept 2016, acquired by Google)
    ● Powered by Machine Learning:
    ○ Natural Language Understanding (NLU)
    ○ Intent Matching
    ○ Conversation Training
    ● Cross platform
    ● Build faster with the Web UI
    ● Scalable: separate your conversation text from
    code
    ● Speech / Voice Integration
    ● Multi-lingual bot support (20+ languages)
    ● Direct integration with 15+ channels like Google
    Assistant, Slack, Twilio, Facebook...
    Development
    suite for building
    Conversational
    UIs.
    

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17. Dialogflow for
    Enterprises
    ● Built on Google Cloud Platform
    infrastructure, easy integration with over
    100 Cloud components
    ● Cloud Support and SLA available
    ● Compliance
    ● Extensive Documentation and training
    programs available.
    ● Powerful IAM; User Roles and Permissions
    ● Stackdriver integration for automatic
    logging, debugging, tracing and error
    reporting
    ● Unlimited API call quotas
    

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18. Types of Chatbots
    and the use cases
    

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19. Three types of chatbots
    Chatbots
    Chatbots in websites, apps, or on
    social media like Facebook
    Messenger, Slack....
    Voice Activated Speakers
    Smart Assistants, like Google
    Assistant, Alexa, Siri, on mobile
    phones and devices like Google
    Home, Google Hub, Watches, TVs...
    Callbots
    Chatbots integrated in IVR systems,
    phone reservation systems, contact
    centers...
    

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20. Chatbot / Web Chatbot
    ● Chatbots for internal processes.
    ● Chatbots that face customers.
    ● Chatbots to collect feedback.
    ● Topic Modelling
    ● Chatbots for intent matching
    (Natural Language for Searching on
    websites.)
    ● Chatbots on social media.
    

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21. Use Case: ING Bank
    Public facing chatbot Inge of ING
    Bank. Customers can ask ‘Inge’
    information about their accounts
    and debit cards.
    Inge can detect the sentiment.
    When customers get frustrated,
    it will automatically route the user
    to a human agent.
    

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22. Architecture: Chatbots
    Dialogflow
    Enterprise
    Website
    Human
    Agent
    

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23. Google Assistant Action
    ● Voice interface will be the future,
    since it’s very accessible.
    ● Google Assistant has over 1 billion of
    users.
    ● According to Adobe Analytics, 71%
    of owners of smart speakers use
    voice assistants at least daily, and
    44% using them multiple times a
    day.
    ● Extend the Google Assistant with
    your apps. Users expect your brand
    to be available on smart speakers.
    

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24. Use Case: Rabobank
    Ok Google, talk to Rabobank.
    The Rabobank Assistant can help
    you with banking via voice. You
    can request your balance,
    transfer money or set budget
    notifications.
    It’s available in multiple languages
    for Google Assistant on mobile
    devices and on the Google
    Home.
    

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25. Confidential + Proprietary
    Confidential + Proprietary
    You will need to design your conversation
    

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26. 26
    Website
    Filter on account name
    or account number
    Lot’s of results on a screen.
    

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27. 27
    Website with Natural Language...
    Natural way of asking!
    

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28. 28
    How much have I spent on
    taxis last month?
    It looks like, you spent about
    20 euros on taxis last month.
    You took the TCA taxi twice.
    Voice channels
    There’s no screen.
    Focus on the conversation.
    

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29. 29
    It looks like, you spent about 20 euros
    on taxis last month. You took the TCA
    taxi twice.
    Here’s an overview:
    Voice channels with screens
    How much have I spent on
    taxis last month?
    Focus on the conversation.
    But also display stuff.
    

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30. 30
    Assistance is not just about voice
    

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31. Confidential + Proprietary
    Confidential + Proprietary
    What happens under the hood?
    

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32. Google | Proprietary & Confidential 32
    GOOGLE ASSISTANT
    USER
    Hey Google..
    Idle
    

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33. Google | Proprietary & Confidential 33
    GOOGLE ASSISTANT
    USER
    Hey Google..
    ..will it rain
    today?
    Listening
    

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34. Google | Proprietary & Confidential 34
    GOOGLE ASSISTANT
    USER WEB SERVER
    Hey Google..
    ..will it rain
    today?
    GET
    www.weather.com/info
    city: Amsterdam
    Date: 2019-02-06
    Recognizing
    

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35. Google | Proprietary & Confidential 35
    GOOGLE ASSISTANT
    USER WEB SERVER
    Hey Google..
    ..will it rain
    today?
    GET
    www.weather.com/info
    city: Amsterdam
    Date: 2019-02-06
    {
    location: “amsterdam”
    weather: “rain”,
    temperature: 8
    }
    Thinking
    

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36. Google | Proprietary & Confidential 36
    GOOGLE ASSISTANT
    USER WEB SERVER
    Hey Google..
    ..will it rain
    today?
    GET
    www.weather.com/info
    city: Amsterdam
    Date: 2019-02-06
    {
    location: “amsterdam”
    weather: “rain”,
    temperature: 8
    }
    Yes, it will rain
    in Amsterdam
    all day today.
    Speaking
    

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37. Confidential + Proprietary
    Confidential + Proprietary
    How can you build your own action
    on top of the Google Assistant?
    

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38. 38
    3rd party integration
    Extend the Google
    Assistant
    with your own custom
    actions.
    Hey Google, let me talk to BookAMeetingRoom
    Welcome, how can I help you?
    I want to book a meeting room
    for 3 persons.
    Let’s get BookAMeetingRoom
    Sure, for when?
    Tomorrow, from 2pm to 3pm.
    

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39. 39
    Ok Google, talk
    to __________.
    Ok Google, connect
    me with __________.
    Ok Google, get
    __________.
    Start a 3rd party action
    There is an app directory!
    (appstore).
    And the Google Assistant can
    give app suggestions.
    

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40. 40
    ● Write the conversation - Dialogflow (Enterprise)
    ● Deploy on GA+ UX components - Actions on Google
    Optional:
    ● Back-end integration - Fulfillment app (dialogflow/aog SDK)
    ● Communication to back-ends - Your own APIs
    What do I need to build
    my own action?
    

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41. 41
    ● Bring your agents to smart speakers (Google
    Home) or phones (Android, iOS app)
    ● Actions on Google includes:
    ○ UI toolkit,
    ○ Audio toolkit
    ○ Account Linking API
    ○ SDKs
    ○ tutorial guides
    ● UI components such as:
    ○ Buttons, Images
    ○ Cards, Carousels,
    ○ Lists
    ○ Tables
    Program for developers of
    Actions (“apps”) that run via
    Google Assistant
    Actions on Google
    

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42. Architecture: Google Assistant
    Dialogflow
    Enterprise
    Google
    Assistant
    

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43. Contact Center
    Frustrations
    ● Long waiting / hold times
    ● Unlimited Call transfers
    ● IVR difficult to navigate
    ● Availability
    ● Inadequate information
    ● Agents have to answer
    same types of questions
    over and over again.
    

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44. With AI in your Contact Center
    Bots that listen and give
    on screen solutions to the
    human agent.
    ● Always answers the
    right question.
    ● Shorten hold times
    ● Shorten the call time
    Bots that understand your
    question.
    ● No longer you need to
    listen to audio
    recordings & press keys.
    ● You don’t need to be
    transferred from one
    agent to the other
    Bots that can answer /
    resolve common
    questions.
    ● Shorten hold times
    ● Shorten the call time
    ● Availability
    ● No longer you’ve been
    told to look on the
    website
    

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45. Use Case: Health Insurance
    The (health) insurance sector
    deals with contact center spikes.
    At the end of the year, customers
    are able to change their
    insurance. Which results in long
    waiting times and students that
    aren’t trained, picking up the
    phone.
    Calls needs to be monitored, to
    gather analytics about the type
    of questions and provided
    service.
    

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46. Architecture: Contact Center without AI
    Call Center
    Agent
    

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47. Architecture: Contact Center
    Dialogflow
    Enterprise
    Agent
    Assist
    Call Center
    Agent
    Text to
    S Speech
    

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48. Demo’s
    

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49. Babs the Banking Bot
    Web Chat Google Assistant
    Hey Google, let me talk to Babs The Banking Bot
    Welcome, how can I help you?
    I want to transfer money.
    Let’s get Babs the Banking Bot
    How much do you want to transfer?
    100 euro.
    

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50. Which customers are unhappy and why?
    (Analytics)
    

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51. How can I improve the user experience?
    (Analytics)
    

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52. Collect real-time chats
    from Dialogflow SDK
    

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53. Mask sensitive
    Information
    with DLP API
    

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54. Understand the text
    with NLP API
    

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55. Store all data in
    a data-warehouse
    

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56. Optimize your agent
    

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57. Confidential + Proprietary
    Advanced Chatflow with machine learning bot analytics
    User types to custom UI
    or channel
    Chatbot replies
    Dialogflow
    Enterprise
    Customer Client
    JS Angular 5 web front-end
    Kubernetes Engine
    Chat Server
    Dialogflow SDK / socket.io
    Kubernetes Engine
    Back-end CRM
    Python / Django
    Kubernetes Engine
    Container
    Registry
    Containers images can be
    stored in the Container Registry
    Messaging Publisher
    Pub/Sub
    Webhook
    Router
    Cloud Function
    Webhook
    Container
    Builder
    Building Dev
    Pipelines
    

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58. Confidential + Proprietary
    Advanced Chatflow with machine learning and bot analytics
    User types to custom UI
    or channel
    Chatbot replies
    Dialogflow
    Enterprise
    Customer Client
    JS Angular 5 web front-end
    Kubernetes Engine
    Chat Server
    Dialogflow SDK / socket.io
    Kubernetes Engine
    Back-end CRM
    Python / Django
    Kubernetes Engine
    Subscription
    Cloud Function
    Sensitivity
    Filter
    DLP API
    Sentiment
    Detector
    NLP API
    Data
    Warehouse
    BigQuery
    Messaging Publisher
    Pub/Sub
    Webhook
    Router
    Cloud Function
    Webhook
    

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59. Cloud AI: Make your workloads
    smarter
    Lee Boonstra
    Sales engineer Google Cloud
    

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60. Why now?
    ●
    Amount of data Better Models More Computing Power
    

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61. Machine
    Learning to
    classify
    things
    Dog vs. Mop
    Oh, that’s easy...
    

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62. Wait what?!
    

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63. We would need
    machine learning
    to give us results
    Confidence level
    

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64. It’s inspired by
    how our
    brains work
    Neural Networks
    

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65. It’s easier to make computers learn
    than to build smarter computers
    

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66. Why Google?
    Data Scientists use Tensorflow
    Tensorflow is what we use for our own internal machine
    learning projects, and now it’s available to you!
    Google made it open source.
    ● More than 480 contributions
    ● 10,000 commits in a year
    ● 53k star rating
    http://www.tensorflow.org
    

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67. Google is an AI company
    Used across products:
    Unique project directories
    Time
    

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68. Confidential + Proprietary
    Confidential + Proprietary
    What’s the state of the industry?
    

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69. Very few people can create
    custom ML models today
    Who can actually
    use AI today?
    10K
    DL researchers
    2M
    ML experts
    +23M
    Developers
    +100M
    Business users
    

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70. Compute is Critical
    80% of recent AI advances
    can be attributed to more
    available computing power
    Dileep George,
    Cofounder of the Machine Learning Startup Vicarious
    

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71. CLOUD AI PLATFORM’S GOAL
    Enable generalist software engineers to easily
    build and run custom AI applications anywhere.
    

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72. How do you go from data
    to intelligent actions?
    

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73. Three ways to go from data to intelligent actions
    Train Custom ML Model
    ● Need a custom ML model
    ● Have a team of data scientist
    ● Run Hybrid / On-premise
    Pretrained Google ML Models
    ● Don’t have much data
    ● Have a team of developers
    ● Run as full AI solution (no
    developers or data scientists
    needed)
    Retrain a Google ML Model
    ● Need a custom ML model
    ● Have a team of developers
    

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74. Introducing Cloud AI
    Less ML
    expertise
    More ML
    expertise
    Cloud AI solutions
    Cloud
    Job Discovery
    Contact
    Center
    Document
    Understanding
    Cloud AI building blocks
    Cloud AI platform
    Cloud ML
    Engine
    Cloud Video
    Intelligence
    ML professionals & service partners
    ASL
    Professional Services
    Organization
    Cloud
    AutoML Vision
    Vision
    Cloud
    Vision
    Language
    Cloud Natural
    Language
    Cloud
    AutoML NL
    Dialogflow
    Enterprise
    Cloud
    Translation
    Cloud
    Speech-to-Text
    Cloud
    Text-to-Speech
    Cloud AutoML
    Translation
    New
    New
    New
    Cloud
    GPU
    Cloud
    TPU
    Cloud
    Dataflow
    Cloud
    Dataproc
    Machine & Deep
    Learning
    ML accelerators ML libraries
    Tensorflow Kubeflow
    Kaggle/datasets
    Datasets
    

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75. Train a custom machine learning model
    Support for
    custom ML Models
    Cloud AI Platform
    Hardware optimised
    for your problem
    Cloud TPU, GPU, CPU
    Any ML framework
    Cloud ML Engine
    Managed Portable
    Portable & Open
    Kubeflow
    One stop AI catalog
    AI Hub
    

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76. ML Engine - Managed Machine Learning
    End to End Machine Learning pipeline
    Data
    ingestion
    1
    Data
    analysis
    2
    Data
    transformation
    3
    Train
    4
    Model
    evaluation
    5
    Model
    validation
    6
    Deploy
    7
    Pub/Sub Data studio
    Datalab
    Dataproc
    Dataflow
    Dataprep
    BigQuery
    

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77. ML Engine - Managed Machine Learning
    End to End Machine Learning pipeline
    Data
    ingestion
    1
    Data
    analysis
    2
    Data
    transformation
    3
    Train
    4
    Model
    evaluation
    5
    Model
    validation
    6
    Deploy
    7
    ● Managed service to make training &
    prediction easy
    ● Easy distributed training
    ● Hyperparameter tuning
    ● Top 4 frameworks
    ● Custom container support coming soon
    

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78. Kubeflow
    Run portable & scalable ML workloads on Open Source Kubernetes
    Easy to get started
    ● Out-of-box support for top frameworks
    ○ pytorch, caffe, tf and xgboost
    ● Kubernetes manages
    dependencies, resources
    Swappable & scalable
    ● Library of ML services
    ● CPU, GPU, TPU
    ● Massive scale
    Meet customer where they are
    ● GCP
    ● On-prem
    ML microservices
    Kubernetes
    Cloud
    On-prem
    Training Predict
    Training Predict
    …
    …
    

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79. AI Hub
    One stop AI catalog
    Easily discover plug & play pipelines & other
    content built by Google AI.
    01
    Private hosting
    Host pipelines and ML content with private
    sharing controls within an enterprise to foster
    reuse within organizations.
    02
    Easy deployment on GCP and hybrid
    Deploy pipelines via Kubeflow on GCP
    and on premise.
    03
    (g.co/aihub)
    

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80. What is included?
    Kubeflow
    (On premises)
    AI Platform
    Integrated with
    Deep Learning
    VM Images
    Cloud
    Dataflow
    Cloud
    Dataproc
    Google
    BigQuery
    Cloud
    Dataprep
    Google Data
    Studio
    Notebooks
    Data Labeling
    Training Predictions
    Pre-built
    Algorithms
    For data
    warehousing
    For data
    transformation
    For data
    cleansing
    For Hadoop and
    Spark clusters
    For BI
    dashboards
    AI Hub
    

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81. Cloud Datalab
    OSS Jupyter notebook
    GCP integrations (BigQuery,
    Cloud Storage, etc)
    Run locally or powered
    by GCE
    

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82. Cloud TPU Offering
    Cloud TPU v2
    180 teraflops
    64 GB HBM
    training and inference
    Cloud TPU v2 PodALPHA
    11.5 petaflops
    4 TB HBM
    2-D toroidal mesh network
    training and inference
    Cloud TPU v3ALPHA
    420 teraflops
    128 GB HBM
    training and inference
    

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84. Use Pre-trained Google machine
    learning model
    Sight
    Cloud Vision
    Cloud Video
    Intelligence
    Language
    Cloud Translation
    Cloud Natural
    Language
    Conversation
    Cloud Speech-to-Text
    Dialogflow Enterprise
    Edition
    Cloud Text-to-Speech
    

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85. Speech API (STT)
    Powered by deep
    learning neural
    networking to power
    your applications..
    No need for signal
    processing or noise
    cancellation before
    calling API. Can
    handle noisy audio
    from a variety of
    environments.
    Noise Robustness
    Can provide context
    hints for improved
    accuracy. Especially
    useful for device and
    app use cases.
    Word Hints
    Speech Recognition
    Recognizes over 80
    languages & variants.
    Can also filter
    inappropriate content
    in text results
    Over 80 languages
    Can stream text
    results, returning
    partial recognition
    results as they
    become available.
    Can also be run on
    buffered or archived
    audio files.
    Real-time results
    

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86. API Usage: Understand Speech -
    Batch
    Stored
    Audio Recognized
    text
    Speech API
    Create JSON
    request with the
    audio file and
    language of audio
    (default is en_US)
    Process
    the JSON
    response
    Call the
    REST API
    1 2 3
    

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87. API Usage: Understand Speech -
    Streaming
    Streaming
    Audio
    Speech API gRPC
    Recognized
    Text
    gRPC streaming
    request with
    initial context
    Real time
    streaming
    results while
    speaking
    Bi-directional:
    Streams audio
    in while stream
    text out
    1 2 3
    

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88. 88
    ● Native audio responses, complementing existing
    Speech-to-Text capability.
    ● Uses DeepMind’s WaveNet technology (It closes
    the voice-quality gap with human voice (based
    on the Mean Opinion Score for voice quality) by
    over 70 percent.)
    ● Also used for Phone Calls, when making use of
    the Phone Gateway.
    ● Device Profiles (Shape the waveform differently,
    depending on the speaker you use.)
    ● You will need to enable Automatic Text to
    Speech in Settings/Speech menu.
    Text to Speech (TTS)
    

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89. 89
    ● Deep generative model of raw audio waveforms
    ● Voices sound natural and unique
    ● Capture subtleties like pitch, pace, and all the pauses that
    convey meaning
    ● Create New voices in weeks i.s. Months
    https://deepmind.com/blog/wavenet-generative-model-raw-audio/
    DeepMinds WaveNet Technology
    

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90. Natural Language API
    Identify entities and label by
    types such as person,
    organization, location,
    events, products and media.
    Enables you to easily
    analyze text in multiple
    languages including
    English, Spanish and
    Japanese.
    Extract tokens and
    sentences, identify parts of
    speech (PoS) and create
    dependency parse trees for
    each sentence.
    Syntax analysis
    Entity Recognition Multi-Language Support
    Understand the overall
    sentiment expressed in a
    block of text.
    Sentiment Analysis
    

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91. DLP API (Data Loss Prevention)
    ● Mask, redact, and generalize PII and sensitive data
    with Machine Learning
    ● Scan for and anonymize sensitive data to comply
    with regulations or policies (text, text on file
    system, Cloud Storage, DataStore, BigQuery)
    ● Clear reporting for review and auditing
    

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92. Translation API
    100+ languages: from
    Afrikaans to Zulu.
    Automatically identify
    languages wiht high
    accuracy.
    Easy to use Google REST
    API. You don’t have to
    extract text from you
    document. Just send it
    HTML documents and get
    back translated text.
    Can seamlessly scale with
    almost any volume. If a
    higher quota is needed, you
    can simply request an
    increase.
    Highly scalable
    The Premium edition is
    tailored for users who need
    precise, long-form
    translation services (e.g.
    livestream translations, high
    volume of emails, detailed
    articles and documents)
    Premium edition BETA
    Detect + translate Simple integration
    TRY THE API
    

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93. Vision API
    Detect broad sets of
    categories within an image,
    ranging from modes of
    transportation to animals.
    Analyze facial features to
    detect emotions: joy,
    sorrow, anger.
    Detect logos.
    Detect and extract text
    within an image, with
    support for a broad range of
    languages, along with
    support for automatic
    language identification.
    Extract text
    Detect different types of
    inappropriate content from
    adult to violent content.
    Powered by Google Safe
    Search
    Detect inappropriate content
    Object Recognition Facial sentiment & logos
    

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94. Video Intelligence API
    Detect entities within the
    video, such as "dog",
    "flower" or "car".
    You can now search your
    video catalog the same way
    you search text
    documents..
    Extract actionable insights
    from video files without
    requiring any machine
    learning or computer vision
    knowledge.
    Enable Video Search
    Label Detection Insights From Videos
    

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95. Demo’s
    

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96. Use Pre-trained Google machine
    learning model
    Sight
    AutoML Vision
    AutoML Video
    Intelligence
    Language
    AutoML Natural
    Language
    AutoML Translation
    Structured Data
    AutoML Tables
    

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97. Retrain a Google machine learning model
    Cloud AutoML
    Dataset
    Train Deploy Serve
    Generate predictions
    with a REST API
    

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98. Auto ML
    Custom task
    Generic task
    Someone else has
    solved this before
    Trained on common
    classes
    Specific to
    your dataset
    ML APIs TensorFlow
    “cat”
    “bob”
    AUTO ML
    Developer
    Data Scientist
    Developer or
    Data scientist
    

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99. UPDATE
    DEPLOY
    EVALUATE
    TUNE ML MODEL
    PARAMETERS
    ML MODEL DESIGN
    DATA
    PREPROCESSING
    Introducing Cloud AutoML
    A technology that can automatically create a Machine Learning Model
    UPDATE
    DEPLOY
    EVALUATE
    TUNE ML MODEL
    PARAMETERS
    ML MODEL DESIGN
    DATA
    PREPROCESSING
    

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100. Via a web interface you will
     go through these steps
     1 3 4
     5
     Upload Labeled
     Data
     Evaluate Predict
     Iterate
     (if needed)
     2
     Train
     

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101. Optimize the pre-trained
     models with your own data
     Auto ML
     ● Create models for your own domain, but use the Google
     pre-trained models as a base.
     ○ Vision, Translation, or NLP
     ● Web Interface, to upload a CSV with labeled data.
     ● Your use-case is:
     ○ Not supported by pre-built APIs AND
     ○ Has sufficient labeled training data
     ● You want to get to produce a model and predict quickly
     ● You don’t want to build a model from scratch
     

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102. Demo’s
     

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103. Conclusion
     

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104. Confidential + Proprietary
     Wouldn’t it be nice to build
     one AI solution that can answer all questions
     and is available from anywhere
     

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105. BigQuery
     Dialogflow
     Enterprise
     Text to
     S Speech
     Google
     Assistant
     Website
     Social media
     Channel
     Agent
     Assist
     Call Center
     Agent
     Your System
     Social media
     Channel
     

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106. Confidential + Proprietary
     But if even if you just add one new AI channel.
     You can improve your customer experience
     and trim business costs.
     

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107. Like smart assistants...
     Dialogflow
     Enterprise
     Google
     Assistant
     Call Center
     Agent
     

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108. Thank you!
     My Examples
     http://www.futurebank.nl
     https://github.com/savelee/kube-django-ng
     https://github.com/GoogleCloudPlatform/tulip
     My Blue print
     https://cloud.google.com/blog/products/ai-machine-learnin
     g/simple-blueprint-for-building-ai-powered-customer-servi
     ce-on-gcp
     

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