English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 0h 58m | 102 MB
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.
Topics include:
- Benefits and shortcomings of GCP
- Enterprise and multicloud integrations
- Comparing GCP technology options
- Outlining solutions for various problems
- Analyzing use cases and best fits
Table of Contents
Introduction
1 Architecting data science
2 Use case (UC) notes
Architecting in GCP
3 GCP benefits
4 GCP shortcomings
5 Enterprise Cloud integration
6 Multicloud integration
UC1 Cloud Data Archive
7 UC1 Analyzing the problem
8 UC1 Outlining the solution
9 UC1 Considering technologies
10 UC1 Laying out the architecture
UC2 Log Analytics
11 UC2 Outlining the solution
12 UC2 Considering technologies
13 UC2 Laying out the architecture
UC3 Customer Analytics
14 UC3 Analyzing the problem
15 UC3 Considering technologies
16 UC3 Designing key elements
UC4 Real-Time Mobile Couponing
17 UC4 Analyzing the problem
18 UC4 Laying out the architecture
Conclusion
19 Next steps
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