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Implementation of AI & ML Technology in Indonesia’s Industry

Galuh Sahid
November 29, 2020

Implementation of AI & ML Technology in Indonesia’s Industry

Tech talk DSC Gunadarma - November 29, 2020

Galuh Sahid

November 29, 2020
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  1. Implementation of AI & ML
    Technology in Indonesia’s Industry
    Galuh Sahid
    @galuhsahid | galuh.me
    Tech talk DSC Gunadarma - November 29, 2020

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  2. • Data scientist at a tech company in Indonesia
    • Google Developer Expert in Machine Learning
    • Co-host podcast Kartini Teknologi (kartiniteknologi.id)
    @galuhsahid
    Hi! I’m Galuh.

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  3. Outline
    @galuhsahid
    • What is machine learning?
    • What are the differences between AI, ML, deep learning…?
    • Examples of ML/AI implementation
    • How to implement ML/AI?
    • Workflow
    • Tools & resources
    • Challenges in ML/AI implementation

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  4. @galuhsahid
    Photo by Bram Van Oost from Unsplash

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  5. @galuhsahid
    Photo by Frank V. from Unsplash

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  6. @galuhsahid
    Photo by Bence Boros from Unsplash

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  7. @galuhsahid
    Photo by Nordwood from Unsplash

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  8. @galuhsahid
    Photo by Krsto Jevtic from Unsplash

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  9. It’s an exciting time to learn more
    about machine learning!
    @galuhsahid

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  10. But… what is machine learning?
    @galuhsahid

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  11. A field of study that gives computers the ability to learn without being
    explicitly programmed.
    Arthur Samuel (1959)
    @galuhsahid

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  12. How is machine learning different
    from traditional programming?
    @galuhsahid

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  13. Traditional
    Programming
    Rules
    Data
    Answers
    @galuhsahid

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  14. if pixel[5][7] is black and pixel [5][6]
    is black and pixel [5][8] is black and …:
    if pixel[6][7] is black and pixel[6][7]
    is black and …:
    return “panda”



    else:
    return “cat”
    Photo by Damian Patowski from Unsplash @galuhsahid

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  15. if pixel[5][7] is black and pixel [5][6]
    is black and pixel [5][7] is black and …:
    if pixel[6][7] is black and pixel[6][7]
    is black and …:
    return “panda”



    else:
    return “not cat”
    Photo by Dušan Smetana from Unsplash @galuhsahid

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  16. Machine
    Learning
    Answers
    Data
    Rules

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  17. Answers
    Data
    Panda
    Cat
    Cat
    Photo by Max Baskalov and Zane Lee from Unsplash
    Panda
    @galuhsahid

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  18. ?
    Cat
    ?
    Photo by Cyrus Chew from Unsplash @galuhsahid

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  19. Machine learning? Artificial
    intelligence? Neural networks? Deep
    learning?
    @galuhsahid

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  20. Source @galuhsahid

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  21. Rule engine
    Knowledge
    Graphs
    Source @galuhsahid

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  22. Regression
    Decision Tree
    Random Forest
    Source @galuhsahid

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  23. ML/AI in the industry
    @galuhsahid

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  24. Recommendation system
    ML/AI in the industry
    @galuhsahid

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  25. Object recognition
    ML/AI in the industry
    @galuhsahid

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  26. Sentiment analysis for brand
    monitoring
    ML/AI in the industry
    @galuhsahid
    https://awario.com/sentiment-analysis/

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  27. OCR for ID cards
    ML/AI in the industry
    @galuhsahid
    https://global.faceid.com/products/id-card-recognition

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  28. Anomaly detection
    ML/AI in the industry
    @galuhsahid
    https://deepai.org/machine-learning-glossary-and-terms/anomaly-detection

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  29. ML/AI implementation: Workflow
    @galuhsahid

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  30. Workflow
    Implementation
    @galuhsahid
    Train your
    model

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  31. @galuhsahid
    Define your
    machine
    learning
    problem
    Acquire &
    prepare your
    data
    Train your
    model
    Serve your
    model
    Adapted from Introduction to ML Problem Framing
    Explore your
    data
    Workflow
    Implementation

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  32. Step #1
    Define your machine learning
    problem
    @galuhsahid

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  33. Step #2
    Acquire & prepare your data
    @galuhsahid

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  34. Step #2
    Acquire & prepare your data
    @galuhsahid
    You need to know:
    • What are the types of data that you can use
    • Where to get them
    • How to get to know your data
    • How to prepare your data

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  35. Tabular
    @galuhsahid
    Data Type #1

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  36. Text
    @galuhsahid
    Data Type #2

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  37. Sound
    @galuhsahid
    Data Type #3

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  38. Image
    @galuhsahid
    Data Type #4

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  39. Step #3
    Explore your data
    @galuhsahid

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  40. Exploratory Data Analysis
    Getting to know your data
    - Analyze your data to summarize their main characteristics
    - Examples include: check for basic statistics (e.g. mean, median), missing
    data, outliers
    @galuhsahid

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  41. Feature engineering
    Preparing your data
    - Handling categorical data
    @galuhsahid

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  42. Feature engineering
    Preparing your data
    - Handling outliers
    @galuhsahid

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  43. Step #4
    Train your model
    @galuhsahid

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  44. Step #5
    Serve your model
    @galuhsahid

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  45. Serving
    Implementation
    @galuhsahid
    • Batch
    • Real-time
    • Don’t forget to monitor your model’s performance!

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  46. ML/AI implementation: Tools &
    Resources
    @galuhsahid

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  47. Programming languages
    Implementation
    @galuhsahid

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  48. Programming languages
    Implementation
    @galuhsahid
    SQL

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  49. NLP
    Libraries
    Implementation
    @galuhsahid
    Deep learning libraries
    Computer vision
    Data processing & manipulation General machine learning

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  50. Libraries
    Implementation
    @galuhsahid
    Data visualization

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  51. Challenges
    @galuhsahid

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  52. Misconceptions about AI/ML
    Challenges
    @galuhsahid
    Common myths:
    • The most important part in implementing ML is building models
    • Hyperparameter tuning is the only way to improve our model
    • We can get a 100% accuracy
    • ML is the answer for everything

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  53. Data availability
    Challenges
    @galuhsahid
    • ML needs data!
    • However, since the awareness of
    data in the industry is relatively
    new, sometimes companies want
    to jump to implementing AI/ML
    without thinking about whether
    they actually have the data or not
    • What about open data? Indonesian
    open data still needs lots of
    improvement
    https://hackernoon.com/the-ai-hierarchy-of-needs-18f111fcc007

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  54. Data infrastructure readiness
    Challenges
    @galuhsahid
    • Before implementing AI/ML, we
    need to have the data
    infrastructure ready first. Building
    data infrastructure needs planning
    and takes time.
    https://hackernoon.com/the-ai-hierarchy-of-needs-18f111fcc007

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  55. Supporting resources
    Challenges
    @galuhsahid
    • Sometimes the success of an ML
    project also depends on the
    resource available
    • Example: NLP resources for
    Indonesian are limited compared to
    English
    https://github.com/kmkurn/id-nlp-resource

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  56. References

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  57. More machine learning
    • On building ML projects: First Steps Towards Your First Machine
    Learning Project
    • On ML with JavaScript: Machine Learning on the Web
    • On ML with TensorFlow: A Whirlwind Tour of Machine Learning
    with TensorFlow
    @galuhsahid

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  58. Learning resources
    •Deep Learning with Python (book) by François Chollet
    •Machine Learning Glossary
    •Machine Learning Crash Course
    •TensorFlow Tutorials
    •Teachable Machine Tutorials (1, 2, 3)
    •But what is a neural network? (video)
    @galuhsahid

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  59. Thank you!
    @galuhsahid

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