The presentations of one day informatic: AI

1. Artificial Intelligence
in Industry
Ilham Bintang, S.Kom., M.Kom.
Universitas Bumigora

2. Machine Learning Enjoyneer
Careers:
- ML Engineer at GleeTrees Singapore (RPA Software)
- ML Engineer at Keyreply Singapore (AI Chatbot)
- ML Engineer at Pahamify Indonesia (EdTech)
- Data Science part time tutor at Purwadhika (Bootcamp)
- ML Engineer at tiket.com Indonesia (Online Travel Agent)
Education:
- 2015, Teknik Informatika - Universitas Mataram
- 2019, Magister Teknologi Informasi - Bina Nusantara
tiket.com Data Science and Machine
Learning Engineering team

3. Agenda
- Data as a new resources
- Artificial Intelligence
- AI in the industry
- Building AI

4. “Big” data

5. The Internet in 2023

6. 3v of big data
(ever expanding
horizon)

7. Sumber
daya
Alam
Sumber
daya
Manusia
Sumber
daya
Data
“Data is a new
resources”

8. After having a lot of
data,
What next?

9. Machine Learning

10. How human learning
Bayi:
- Mengenal wajah orang tua
- Mengenal objek-objek umum
- Membedakan suara
- Mengerti tata bahasa
- Mengenal kosakata
- Membedakan beragam objek

11. How machine learning
Machine:
- Mengenal wajah orang (matrix)
- Mengenal objek-objek umum (matrix)
- Membedakan suara (matrix)
- Mengerti tata bahasa (matrix)
- Mengenal kosakata (matrix)
- Membedakan beragam objek (matrix)

12. Human vs Machine
human machine

13. AI in daily life

14. Building data for AI

20. We know how to build
the data, so what?

25. Stages of AI

26. AI in the future

27. Narrow AI

28. General AI
Ordering flight and hotel using query
and it will search from the flight and
hotel API.
Optimize python code

29. General AI
Can do multiple things using
single model

30. AI trends is pushing to general AI and Super AI

31. Building AI in Industry

32. AI development phase

33. AI creation and development

34. AI based on the data type
Computer
Vision
NLP Speech Tabular

35. AI Maturity Model

36. AI product in tiket.com

37. Smart global search
We enhance user experience by giving relevant result through global search

38. Customized search result
Every user is unique. The search result is adjusted based on user profile

39. Hotel image quality assessment
Only the best and most interesting image will be set as thumbnail

40. Hotel image quality assessment
Only the best and most interesting image will be set as thumbnail

43. Similar hotel recommendation
For better cross selling

45. - User review analysis
- Property scoring
- Customer segmentation
- Property duplicate detection
- etc

46. In addition, we cover a lot of AI
domain including NLP, Computer
Vision, Information retrieval,
Recommendation System,
and many more……

47. AI Trends.

50. AI Booming

52. “The AI Hype Cycle has many innovations
that deserve particular attention within the
two-to-five-year period to mainstream
adoption that include generative AI and
decision intelligence,”
says Gartner Director Analyst Afraz Jaffri.
“Early adoption of these innovations will
lead to significant competitive advantage
and ease the problems associated with
utilizing AI models within business
processes.”

53. Gen AI Landscape

55. Where do I
start?

56. Data role

57. Data Workflow

58. Data storage

60. Data Science Tools
- Data science tools divided to few part:
- Data processing that support parallel processing (NVTabular, Dask, Spark)
- Data warehouse (Big Query, RedShift, Snowflake)
- Model development (Tensorflow, Pytorch)
- DS Workflow: Kubeflow

61. Data Science “pokemon”

62. Monitoring

63. Experimentation
Source: mlflow docs. Not an internal experiment

64. And Jupyter. Jupyterlab, pytorch, tensorflow is a daily buddy
Source: jupyternotebook docs. Not an internal experiment

65. QnA