This course focuses on the end-to-end design of a cloud architecture, specifically from the perspective of optimizing that architecture for Big Data processing, real-time analytics, and real-time prediction using ML and AI.
The Google Cloud Platform offers up a very large number of services, for every important aspect of public cloud computing. This array of services and choices can often seem intimidating - even a practitioner who understands several important services might have trouble connecting the dots, as it were, and fitting together those services in meaningful ways. In this course, Designing Scalable Data Architectures on the Google Cloud, you will gain the ability to design lambda and kappa architectures that integrate batch and streaming, plan intelligent migration and disaster-recovery strategies, and pick the right ML workflow for your enterprise. First, you will learn why the right choice of stream processing architecture is becoming key to the entire design of a cloud-based infrastructure. Next, you will discover how the Transfer Service is an invaluable tool in planning both migration and disaster-recovery strategies on the GCP. Finally, you will explore how to pick the right Machine Learning technology for your specific use. When you’re finished with this course, you will have the skills and knowledge of the entire cross-section of Big Data and Machine Learning offerings on the GCP to build cloud architectures that are optimized for scalability, real-time processing, and the appropriate use of Deep Learning and AI technologies. Topics:
- Course Overview
- Implementing Integrated Batch and Streaming Architectures on the GCP
- Designing Migration and Disaster Recovery Strategies on the GCP
- Designing Robust ML Workflows on the GCP