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LinkedIn Learning

Data Science on Google Cloud Platform: Architecting Solutions

via LinkedIn Learning


Learn how to architect data science solutions at scale using the capabilities provided by Google Cloud Platform (GCP).

Data science is an application area that's exponentially growing, consuming huge amounts of data and making revolutionary predictions. At the same time, Google Cloud Platform (GCP) is fast tracking the cloud movement by providing cutting-edge tools and options. In this course, learn how to architect data science solutions on GCP and harness the power of these two technologies for your business. Instructor Kumaran Ponnambalam starts off by reviewing technology options available in GCP for executing various data science processes, as well as the benefits and shortcomings of this suite of cloud computing services. He then analyzes different technologies and steps through the architecture building process for various use cases, including customer analytics and real-time mobile couponing.


  • Architecting data science
  • Use case (UC) notes
1. Architecting in GCP
  • GCP benefits
  • GCP shortcomings
  • Enterprise: Cloud integration
  • Multicloud integration
2. UC1: Cloud Data Archive
  • UC1: Analyzing the problem
  • UC1: Outlining the solution
  • UC1: Considering technologies
  • UC1: Laying out the architecture
  • UC1: Designing key elements
3. UC2: Log Analytics
  • UC2: Analyzing the problem
  • UC2: Outlining the solution
  • UC2: Considering technologies
  • UC2: Laying out the architecture
  • UC2: Designing key elements
4. UC3: Customer Analytics
  • UC3: Analyzing the problem
  • UC3: Outlining the solution
  • UC3: Considering technologies
  • UC3: Laying out the architecture
  • UC3: Designing key elements
5. UC4: Real-Time Mobile Couponing
  • UC4: Analyzing the problem
  • UC4: Outlining the solution
  • UC4: Considering technologies
  • UC4: Laying out the architecture
  • UC4: Designing key elements
  • Next steps

Taught by

Kumaran Ponnambalam

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