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1. Amr Koura
End-to-End machine learning
pipeline

2. What is ML?

3. Input Algorithm Output
Input Algorithm
Output

4. ML pipeline

6. Recommender System

7. What is Recommender System
• ML model helps users to discover new product or service.
• Rank items.
• Example:
Amazon.com
Netflix movie recommender system
Google news
Facebook friends Recommendation.
• We will build end-to-end recommender system (content based algorithm)

8. Prerequisite

9. Content based recommender
system

10. Content based Recommender
User-item-ratings
User ID Movie ID Rating
User ID Movie ID Rating
1 1 5
1 2 5
1 3 1
Example

11. Content based Recommender
Item Profile:
Movie # Genre Language Actors
Movie # Genre Language Actors
1 Comics Ar Henedy
2 Comics Ar Henedy
3 Horror Eng XYZ
4
…
N
Example

12. Content based Recommender
User profile
User ID Genre Language Actors
Example:(Weighted average of item profiles)
Movie # Genre Language Actors
1 Comics Ar Henedy
2 Comics Ar Henedy
3 Horror Eng XYZ
4
…
N
User ID Movie ID Rating
1 1 5
1 2 5
1 3 1

13. Content based Recommender
User profile
User ID Movie Type Language Actors
1 X Y Z
Cosine similarity
Movie # Genre Language Actors
1 Comics Ar Henedy
2 Comics Ar Henedy
3 Horror En XYZ
4
…
N

14. Model Evaluation

15. Content based Recommender
Evaluation
Full Dataset
Movie # Genre Language Actors
1 Comics Ar Henedy
2 Comics Ar Henedy
3 Horror Eng XYZ
4
…
N
User ID Movie ID Rating
1 1 5
1 2 5
1 … …
1 100 1
Full Interactions
User ID Movie ID Rating
1 1 5
1 2 5
1 … …
1 80 1
User ID Movie ID Rating
1 1 5
1 2 5
1 … …
1 20 1
Training dataset Testing dataset

16. Content based Recommender
Recall@K
5
100%
5 5
80%
4 5
60%
3 5
20%
1
Relevant Recommended

17. Deployment

18. Build docker
image
Push Docker
image to AWS
ECR
Run ECS
cluster

19. ML is simple