This document discusses MLOps and introduces TensorFlow Extended (TFX) as a solution for implementing MLOps. It discusses the need for MLOps to operationalize machine learning, including challenges around data, models, development and compliance. TFX is presented as integrating data processing, model building, deployment and monitoring within a unified platform.

1. Introduction to
MLOps in Daily Apps

2. Google:
Trust and Safety
To prevent phishing @
scale with Data Analytics
and ML

3. Proprietary + Confidential
Today’s Agenda
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2
3
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[Summary] What is Machine Learning?
Why ML Ops + Case study?
ML Ops Implementation
Tensorflow Extended (TFX)
QnA

4. What is Machine Learning?

5. Machine Learning
Computational methods using experience to improve performance

6. Machine Learning
Using Computer and data to achieve objective
● Computer → Algorithm, complexity analysis, theoretical guarantees.
● Data analysis→ Statistics, probability
● Achieve Objective → Understanding the problem, simulation, evaluation,
etc

8. Real World Impact
What are the applications of AI and ML?

9. Important Resources (Teachable Machine)
https://teachablemachine.withgoogle.com/train/image

10. Street name
Street number
Street View
Sign
Business facade
Sign
Business name
Traffic light
Traffic sign
Street number

11. Google Translate
11

12. Frame the Problem
What is your goal?
Who are your stakeholders?
How do you add value to them?

13. ML Pipeline
Data Collection +
Preprocessing
Model Training
and Evaluation
Machine Learning
Operations (MLOps)

14. Why ML Ops?

15. ML Ops
Operating real ML for real Use Case
1. Model Push
2. Model Validation
3. Monitoring/Anomaly Detection

16. End Goal of ML Values
Implement Ideas to Reality
“More than 87% of data
science projects never make it
into production”
Source: VentureBeat

17. End Goal of ML Values
Implement Ideas to Reality (Source)
Organization - Managing different ecosystems like programming languages
Compute - Continuous and reliable compute resources (dedicated servers or cloud only option)
Portability - Huge dependencies on legacy systems
Seasonality - ML workloads works in patches; this need autoscaling capabilities

18. End Goal of ML Values
Implement to Reality

19. End Goal of ML Values
Paying Technical Debt
Technical debt is the ongoing cost of expedient decisions made during code implementation. It is
caused by shortcuts taken to give short-term benefit for earlier software releases and faster time-
to-market. Technical debt tends to compound.
Deferring the work to pay it off results in increasing costs, system brittleness, and reduced rates
of innovation. – D Sculley, in the Hidden Debt in Machine Learning Systems

20. End Goal of ML Values
Paying Technical Debt
1. Entanglement
Changing Anything Changing Everything – CACE
1. Complex pipelines
Many different pipelines interlinked with multiple disciplinaries
1. Hidden feedback loops
ML models affect reality, and reality affect ML Models.
1. Fragile Data dependency
Upstream and downstream dependencies (e.g: data schema error)

21. End Goal of ML Values
You don’t realize it exists.

22. ML Ops Implementation

26. Data

27. ML Model

28. Dev Ops

29. ML Ops = Data + ML Model + Dev Ops

30. ML Ops =
Data + ML Model + Dev Ops
Training
● Information
theory
● Latency
● Interpretability
Testing
● Feedback Loops
● Data Leakage
● Evaluation
metrics
Tuning:
● Optimizer
● Assumptions
Data Eng Pipeline
● Extract
● Transform
● Load
Data Prep
● Wrangling
● Cleaning
Statistical Treatment:
● Data Distribution
● Sampling
● Concept Drift
Continuous dev
● Automated testing
● Version Control
● Deployments env
● Config Control
Continuous integration
● Version Manager
● Package Manager
Monitoring:
● Logs streaming
(kafka)
● Audit
● Dashboard Viz

31. But Wait
Data + ML Model + Dev Ops + Compliance
Training
● Information
theory
● Latency
● Interpretability
Testing
● Feedback Loops
● Data Leakage
● Evaluation
metrics
Tuning:
● Optimizer
● Assumptions
Data Eng Pipeline
● Extract
● Transform
● Load
Data Prep
● Wrangling
● Cleaning
Statistical Treatment:
● Data Distribution
● Sampling
● Concept Drift
Continuous dev
● Automated testing
● Version Control
● Deployments env
● Config Control
Continuous integration
● Version Manager
● Package Manager
Monitoring:
● Logs streaming
(kafka)
● Audit
● Dashboard Viz
Security Testing
● Penetration Testing
● Data Mart Testing
● Malware protection
Social Eng Protection
● Employee Training
● Access Control
Threat Handling:
● Monitoring
● Kill switch
● Dashboard Viz

32. But Wait
Data + ML Model + Dev Ops +
Compliance

33. The solution: TFX Extended
Data + ML Model + Dev Ops + Compliance

34. TFX Extended

35. The solution: TFX Extended

36. The solution: TFX Extended
Data

37. The solution: TFX Extended
ML

38. The solution: TFX Extended
DevOps

39. The solution: TFX Extended
MetaData Store Audit

40. The solution: TFX Extended
1. Dedicated workforce
a. separation of concerns
2. Fast information transfer
a. control
3. Proactive Reaction to Feedback
a. concept drift handling

41. My articles

42. Reach out to me :)
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