About this Course

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Flexible deadlines
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Advanced Level
Approx. 8 hours to complete
English

Instructor

Learner Career Outcomes

25%

started a new career after completing these courses

20%

got a tangible career benefit from this course
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Advanced Level
Approx. 8 hours to complete
English

Offered by

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Google Cloud

Syllabus - What you will learn from this course

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Week
1

Week 1

18 minutes to complete

Welcome to the course

18 minutes to complete
2 videos (Total 8 min), 1 reading
2 videos
Getting Started with Google Cloud Platform and Qwiklabs5m
1 reading
How to Send Feedback10m
2 hours to complete

Architecting Production ML Systems

2 hours to complete
11 videos (Total 31 min), 1 reading, 3 quizzes
11 videos
The Components of an ML System1m
The Components of an ML System: Data Analysis and Validation4m
The Components of an ML System: Data Transformation + Trainer1m
The Components of an ML System: Tuner + Model Evaluation and Validation2m
The Components of an ML System: Serving1m
The Components of an ML System: Orchestration + Workflow3m
The Components of an ML System: Integrated Frontend + Storage1m
Training Design Decisions4m
Serving Design Decisions5m
Designing from Scratch2m
1 reading
Lab Intro: Structured data prediction using AI Platform10m
2 practice exercises
Components of ML Systems
Architecting Production ML Systems4m
1 hour to complete

Ingesting data for Cloud-based analytics and ML

1 hour to complete
7 videos (Total 25 min)
7 videos
Data On-Premise3m
Large Datasets3m
Data on Other Clouds1m
Existing Databases2m
Demo: Load data into BigQuery5m
Demo: Automatic ETL Pipelines into GCP4m
1 practice exercise
Ingesting data for Cloud-based analytics and ML10m
Week
2

Week 2

2 hours to complete

Designing Adaptable ML systems

2 hours to complete
11 videos (Total 33 min)
11 videos
Adapting to Data2m
Changing Distributions3m
Exercise: Adapting to Data1m
Right and Wrong Decisions3m
System Failure1m
Mitigating Training-Serving Skew through Design1m
Lab Intro: Serving ML Predictions in batch and real-time1m
Lab Solution: Serving ML Predictions in batch and real-time8m
Debugging a Production Model3m
Summary59s
1 practice exercise
Designing Adaptable ML Systems5m
1 hour to complete

Designing High-performance ML systems

1 hour to complete
12 videos (Total 46 min)
12 videos
Training5m
Predictions2m
Why distributed training?4m
Distributed training architectures6m
Faster input pipelines3m
Native TensorFlow Operations3m
TensorFlow Records1m
Parallel pipelines6m
Data parallelism with All Reduce4m
Parameter Server Approach2m
Inference3m
1 practice exercise
Designing High-performance ML systems
2 hours to complete

Hybrid ML systems

2 hours to complete
8 videos (Total 47 min)
8 videos
Machine Learning on Hybrid Cloud5m
KubeFlow2m
Demo: KubeFlow21m
Embedded Models2m
TensorFlow Lite2m
Optimizing for Mobile5m
Summary2m
1 practice exercise
Hybrid ML systems
12 minutes to complete

Course Summary

12 minutes to complete
1 video (Total 2 min), 1 reading
1 video
1 reading
Additional Resources10m

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About the Advanced Machine Learning with TensorFlow on Google Cloud Platform Specialization

Advanced Machine Learning with TensorFlow on Google Cloud Platform

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