Google Cloud Professional Data Engineer certification prepares machine learning engineers for running ML models in production. This includes DevOps tasks, such as monitoring and scaling.

1. Google Cloud
Certifications &
Machine Learning
TensorFlow-NorthWest Meetup
January 29. 2020

4. GCP Reference ML Architecture

7. What’s Needed to Deploy ML to
Production
● ML centric
○ Data collection
○ Data
transformation
○ Feature
engineering
○ Model building
and evaluation
● Production centric
○ Data storage
○ Scalable transformation
processes
○ Workflows
○ Integration and
deployment
○ Monitoring
○ Security and Compliance

8. Source: https://www.7wdata.be/data-science/whats-the-difference-between-a-data-engineer-a-data-analyst-and-a-data-scientist/

9. Specifically, what do we need to know?

10. Know What Certifications Test

11. Know What Certifications Test

12. Customer
Implementation
Technical
requirements
Business
requirements
Technical
requirements
Customer
Implementation
Professional
Certification
Scope of GCP Certifications
Associate
Cloud Engineer
Professional Level
Certification
Technical
requirements
Business
requirements
Design
Plan
Ensure Feasibility
Optimize
Build
Deploy
Manage
Customer
Implementation
Build
Deploy
Manage
Technical
requirements
Build
Deploy
Manage

14. Who Benefits?
● Practitioners - salaries 15% more than
non-certified (IDC/Microsoft )
● Organizations - new hire productivity,
hire advancement, streamlined hiring
● Google - supports efforts to expand
market share by creating a pool of
knowledgeable professionals

15. Google Cloud Professional Data
Engineer Exam
○ Designing data processing and storage systems
○ Migrating data warehouse
○ Operationalizing storage, processing infrastructure, and
pipeline
○ Operationalizing ML models
○ Pre-built models
○ ML architecture e.g. edge computing
○ Security and compliance
○ Scalability and portability

16. Designing Data Processing & Storage
Systems
● Data modeling
● Latency, throughput,
transactions
● Fault tolerance
● Distributed systems
● Batch and stream
processing
● Job automation and
orchestration
● Event processing

17. Operationalizing Processing and
Storage
● Storage costs and performance
● Data cleansing
● Data lifecycle management
● Provisioning resources
● Monitoring and adjusting pipelines
● Effective use of managed services

18. Operationalizing Machine Learning
Models
● Pre-built ML models as a service
● Ingesting data
● Training machine learning models
● Training and serving infrastructure
● Hardware accelerators
● ML terminology

19. Options for Operational ML
● Compute Engine
○ GPU or TPUs
○ Deep Learning VM
○ C2-standard-60 60 vCPUs 240GB 257TB
● Kubernetes Engine
○ Supports GPUs and TPUs
○ Containers with TF, PyTorch, and R
○ Job and deployment APIs
● AI Platform
○ Serverless option
○ Train, evaluate, tune models
○ TensorFlow, Scikit Learn, XGBoost

20. Study Strategy
● Follow Certification Exam Guide
○ High level domains & detailed tasks
○ https://cloud.google.com/certification/guides/data-engineer/
● Take Practice Exam
○ Good assessment but the actual test is more difficult
● Identify weakest areas
○ We often focus on some, not all domains in our work
○ You will be tested on all domains
● Perform tasks using Cloud Console and Cloud Shell

21. Exam Taking Strategy
● Timed test, know your remaining time
○ 50 multiple choice questions
○ 2 hours
○ Mark questions for review
● Read questions carefully
○ Identify key services and software
○ Identify technical requirements
● Focus on how to choose between likely options or near misses

22. Services to Know in Depth

23. How to Choose Between
● Cloud SQL
● Cloud Spanner
● Cloud Firestore
● Cloud Bigtable
● BiqQuery
● Cloud Storage

24. Learning Resources

25. Certification Guides
Professional
Data Engineer
April, 2020

26. Final Thoughts ...
● Certifications help
define scope of
knowledge needed
for a particular role
● They are a measure
of competence, not
expertise
● Continuous learning
What you
know
What you think
you know
Circle of Competence

27. Image Sources
Slide 2” https://blog.jupyter.org/jupyterlab-is-ready-for-users-5a6f039b8906.
Slide 3: https://commons.wikimedia.org/wiki/File:Proto-Labs-Manufacturing-Injection-Molding.jpg
Slide 4: https://cloud.google.com/blog/products/ai-machine-learning/solution-implementing-industrial-predictive-maintenance-part-iii
Slide 8: https://www.7wdata.be/data-science/whats-the-difference-between-a-data-engineer-a-data-analyst-and-a-data-scientist/
Slide 10: https://irecusa.org/wp-content/uploads/2013/06/Task-Analysis-word-cloud.jpg
Slide 11 : https://irecusa.org/2013/06/job-task-analyses-at-the-very-heart-of-credentialing/
Slide 13 https://miro.medium.com/max/2048/0*zb4dcNV0gFVgFTJm.png