From the course: Advanced Data Processing: Batch, Real-Time, and Cloud Architectures for AI
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Architecting for the cloud
From the course: Advanced Data Processing: Batch, Real-Time, and Cloud Architectures for AI
Architecting for the cloud
- [Instructor] What is different between creating architectures for local deployments and for the cloud? We have so far discussed the architecture principles for batch and real-time AI in enterprise deployments. These principles still hold good for cloud environments. One key consideration with cloud is cost. If it is not managed, cost can overshoot the budget. So cloud architectures need to balance cost, performance, and scalability, such that the desired performance can be achieved while keeping costs in control. What are the key cloud architecture considerations? First comes scalability. Even if cloud services are horizontally scalable, scale should be controlled so that the deployment does not overshoot the budget. Cost optimization is critical. Only run services when they need to actually do work. Running idling services will incur costs without any benefits. Security and compliance play a key role as enterprise data is stored and used on cloud resources that are typically shared…
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