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Implementation of AI & ML Technology in Indonesia’s Industry

C57af1a97254c871ece1cee87979a222?s=47 Galuh Sahid
November 29, 2020

Implementation of AI & ML Technology in Indonesia’s Industry

Tech talk DSC Gunadarma - November 29, 2020

C57af1a97254c871ece1cee87979a222?s=128

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
  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.
  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
  4. @galuhsahid Photo by Bram Van Oost from Unsplash

  5. @galuhsahid Photo by Frank V. from Unsplash

  6. @galuhsahid Photo by Bence Boros from Unsplash

  7. @galuhsahid Photo by Nordwood from Unsplash

  8. @galuhsahid Photo by Krsto Jevtic from Unsplash

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

    @galuhsahid
  10. But… what is machine learning? @galuhsahid

  11. A field of study that gives computers the ability to

    learn without being explicitly programmed. Arthur Samuel (1959) @galuhsahid
  12. How is machine learning different from traditional programming? @galuhsahid

  13. Traditional Programming Rules Data Answers @galuhsahid

  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
  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
  16. Machine Learning Answers Data Rules

  17. Answers Data Panda Cat Cat Photo by Max Baskalov and

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

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

  20. Source @galuhsahid

  21. Rule engine Knowledge Graphs Source @galuhsahid

  22. Regression Decision Tree Random Forest Source @galuhsahid

  23. ML/AI in the industry @galuhsahid

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

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

  26. Sentiment analysis for brand monitoring ML/AI in the industry @galuhsahid

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

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

  29. ML/AI implementation: Workflow @galuhsahid

  30. Workflow Implementation @galuhsahid Train your model

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

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

  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
  35. Tabular @galuhsahid Data Type #1

  36. Text @galuhsahid Data Type #2

  37. Sound @galuhsahid Data Type #3

  38. Image @galuhsahid Data Type #4

  39. Step #3 Explore your data @galuhsahid

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

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

  43. Step #4 Train your model @galuhsahid

  44. Step #5 Serve your model @galuhsahid

  45. Serving Implementation @galuhsahid • Batch • Real-time • Don’t forget

    to monitor your model’s performance!
  46. ML/AI implementation: Tools & Resources @galuhsahid

  47. Programming languages Implementation @galuhsahid

  48. Programming languages Implementation @galuhsahid SQL

  49. NLP Libraries Implementation @galuhsahid Deep learning libraries Computer vision Data

    processing & manipulation General machine learning
  50. Libraries Implementation @galuhsahid Data visualization

  51. Challenges @galuhsahid

  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
  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
  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
  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
  56. References

  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
  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
  59. Thank you! @galuhsahid