The document presents a case study on implementing machine learning (ML) and natural language processing (NLP) applications within a data platform, focusing on the challenges and solutions in data extraction, transformation, and loading (ETL) processes, along with analytics workflow. It details the architecture for efficiently handling and analyzing review text data, highlighting the importance of separating concerns in system design for better scalability and maintainability. Key insights include the challenges of web-scraping, processing, and the potential for improved resource management through containerization and microservices.

1. ML IN DATA PLATFORM
A Case Study with NLP Application
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2. 2
Table of content
No Content
1 Introduction
2 Data Platform – ETL Process
3 Data Platform – Analytics Workflow
4 Afterthoughts

3. 3
INTRODUCTION
01
1. Introduction to Case Study
2. Introduction to Data Platform

4. 1.1.1. Potential Values of ML/NLP Application
4
- ML applications can bring new-found values
- Case study: Online Review Analytics
- Opinions from others increasingly guide customer's purchases
=> Growth, Improvement, Investment implications
Refs
- https://www.mckinsey.com/industries/consumer-packaged-goods/our-insights/five-star-growth-using-online-ratings-to-design-better-products
- https://www.thinkwithgoogle.com/consumer-insights/consumer-trends/customer-review-preference-statistics/

5. 1.1.2. Dealing with text data
5
- An insight-mining platform for review text is highly valuable. It is difficult though
- Engineering challenges
- Getting the reviews => web-scraping, data collection
- Storing reviews => moving, maintaining, deduplicating large amount of texts
- Processing reviews => text cleaning, processing, and analytics at scale
- Analytics challenges
- Natural Language Processing – NLP
- Insight communication: dashboards and visualization

6. 1.2.1. Data Platform overall architecture
6

7. 1.2.2. Example: output from ETL Process
7

8. 1.3. Example: output from Analytics Workflow
8

9. 1.3. Example: insight communication – Web Application
9

10. 10
ETL PROCESS
02
1. Extract, Transform, Load
2. Data Collection
3. Data Storage

11. 2.1. Extract, Transform, Load
11
- Extract:
- Data Collector: collect data from websites
- Extract and Map from raw data collected
- Transform: clean up data (trim, special characters,…), deduplications, etc.
- Load: to databases for storage and analysis: MongoDB, BigQuery
- Batching: split large amount of data into batches for parallel processing
- Worker: a container that moves/processes data -> Mini-ETL

12. 2.1. Data Collection: web-scraping
12
Web Scraper

13. 2.1. Data Collection: Benefit & Challenge
13
Benefit Challenge
It’s Free
It’s Big Data
Fake Data
- Captcha
- IP Blocking
Hard to collect
- Javascript Rendering

14. 2.1. Data Collection: How to deal with challenges?
14
WEB BROWSER
SELENIUM
PROXY
To avoid IPs blocking & Captcha
To overcome Javascript rendering
Control Browser by Code
Control Browser by Code

15. 2.2. Data Storage
15
- PostgreSQL: store process metadata (used by orchestrator)
- Google Cloud Storage: store intermediary CSV files
- MongoDB: flexible, persistent storage for text documents. Allow easy and frequent
edits
- Google BigQuery: analytics data storage and distributed processing engine using
SQL – familiar language for Data Analysts

16. 16
ANALYTICS WORKFLOW
03
1. First Implementation
2. Inference Services

17. 3.1.1 Analytics Workflow
17
- After ETL process, data is available for further processing and analysis
- Analytics Workflow:
- A part of Data Platform
- Extract information from data for insights
- Machine Learning models are integral part of text analytics
- Information is extracted, and pushed to BigQuery for queries

18. 3.1.2 First implementation
18
- Implement each model as a worker
- Advantages:
- Easy to implement
- Suitable for early stages: fast
implementation and acceptable
performance
- Several drawbacks: technical debts
- Mixing of concerns
- Low flexibility
- Limited scalability

19. 3.1.3 First implementation: mixing of concerns
19
- Data Platform’s intended purpose: moving data, processing, and interacting with
various API on the way => mostly I/O operations
- Computationally-heavy tasks are usually delegated: e.g. to BigQuery
- Mixing I/O and computations

20. 3.1.4 First implementation: scalability
20
- Everything seems ok, until
we must process many
reviews (100,000s -
1,000,000s, various
lengths, can be very long)
- Manual scaling: replicate
workers -> VM
resource/cost constraint
- GPU acceleration? -> ETL
workers don’t need GPU

21. 3.1.5 First implementation: monitoring and maintenance
21
- No real monitoring components for performance degradation
- Data drift, concept drift?
- If needed, model is inspected manually
- Collect, process, re-train models manually
- Upload trained model to GCS, re-deploy workers

22. 3.2.1 Inference Services: separation of concerns
22
- Income Inference Services
- No direct I/O for data, only accept
HTTP requests with input and
response with computed results
=> Easier to maintain and optimize both
ends

23. 3.2.2 Inference Services: overall architecture
23

24. 3.2.3 Inference Services: solving redundancy and reusability
24
- Each ML model is treated as a microservice
- Several ML models can be connected as an inference pipeline for complex tasks
- Promote reusability and flexibility => save resources

25. 3.2.4 Inference Services: solving scalability
25
- Services are containerized, run, and deployed independently
- Can be migrate to any environment with relative ease
- For maximum scalability => K8s cluster (GKE) with autoscaling
- Thanks to K8s, deployment is easier.
- Rollout deployments: no/minimal downtime

26. 3.2.5. Inference Services: monitoring
26
- Metrics are logged to a central data-lake and visualized in a
dashboard.
Image from https://www.datarobot.com/wiki/machine-learning-operations-mlops/

27. 3.2.6. Inference Services: results and drawbacks
27
- Results
- A more flexible and effective solution
- More resilient ETL process: less complex
- Reduced ETL resource consumption and processing time
- New system of services can be developed and maintained separately
- Drawbacks
- Appearance of more infrastructures and tools -> management overhead
- Complex inter-dependency of inference services as it expands
- Requires more expertise in managing K8s clusters and deployment

28. 28
WHAT WE LEARNED
04

29. 4.1. What We Learned?
29
- ML Application can be tricky to be done right
- Not much resources and best practices
- Solved by: thorough analysis of use-cases
- Solved by: proper scoping and sizing
- Separating I/O Intensive from Computationally-intensive tasks
- ETL components
- ML components
- Good architecture design from the beginning can save time and cost later
- Over-engineered vs under-engineered
- Easy in hindsight, difficult in practice
Hope these ideas help you in designing your next ML Application

30. THANK YOU – Q&A