- Benefits and shortcomings of GCP
- Enterprise and multicloud integrations
- Comparing GCP technology options
- Outlining solutions for various problems
- Analyzing use cases and best fits
Skill Level Intermediate
- [Kumaran] Your business knows about data science. You know how to build a team that supports it and even how to analyze the results in a proper way. But, what about everything else in the middle? How do you go about from collecting the data all the way to making business decisions with its analysis? That's where you need expertise in architecting solutions. In all likelihood, your data science applications will be build on cloud platforms, such as AWS, Microsoft Azure, and Google Cloud Platform. That's because cloud brings unlimited scalability and elasticity to data science. Those all cloud platform have their own pros and cons. In this course, I will show you how to architect data science solutions on GCP. I will take four different use cases and navigate through the architecture building process for each of them. You need prior familiarity with the basics of GCP platform as well as software architecture. So, join me, Kumaran Ponnambalam, in my course. Let's explore and experience the options for architecting data science solutions on GCP.