Google Cloud Big Data and Machine Learning Fundamentals

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What Will You Learn?

Identify the data-to-AI lifecycle on Google Cloud and the major products of big data and machine learning.
Design streaming pipelines with Dataflow and Pub/Sub and dDesign streaming pipelines with Dataflow and Pub/Sub.
Identify different options to build machine learning solutions on Google Cloud.
Describe a machine learning workflow and the key steps with Vertex AI and build a machine learning pipeline using AutoML.

About This Course

Provider: Coursera
Format: Online
Duration: 9 hours to complete [Approx]
Target Audience: Beginners
Learning Objectives: By the end of this free course, you will be able to navigate the data-to-AI lifecycle on Google Cloud, proficiently utilizing Vertex AI for building robust big data pipelines and machine learning models.
Course Prerequisites: NA
Assessment and Certification: Earn a Certificate upon completion from the relevant Provider
Instructor: Google Cloud
Key Topics: Google Cloud Platform, Cloud Computing, Bigquery, Tensorflow
Topic Covered: 
  1. - Course introduction
  2. - Google Cloud infrastructure
  3. - Compute
  4. - Storage
  5. - The history of big data and ML products
  6. - Big data and ML product categories
  7. - Getting Started with Google Cloud Platform and Qwiklabs
  8. - Big data challenges
  9. - Message-oriented architecture
  10. - Designing streaming pipelines with Apache Beam
  11. - Implementing streaming pipelines on Cloud Dataflow
  12. - Visualization with Looker
  13. - Visualization with Looker Studio
  14. - Storage and analytics
  15. - Demo: Querying TB of data in seconds
  16. - Introduction to BigQuery ML
  17. - Using BigQuery ML to predict customer lifetime value
  18. - BigQuery ML project phases
  19. - BigQuery ML key commands
  20. - Options to build ML models
  21. - Pre-built APIs
  22. - AutoML
  23. - Custom training
  24. - Vertex AI
  25. - AI Solutions
  26. - Data preparation
  27. - Model training
  28. - Model evaluation
  29. - Model deployment and monitoring

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