From the course: Advanced Data Processing: Batch, Real-Time, and Cloud Architectures for AI
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AI in the cloud
From the course: Advanced Data Processing: Batch, Real-Time, and Cloud Architectures for AI
AI in the cloud
- [Instructor] What are the advantages and shortcomings of doing AI in the cloud? Cloud has revolutionized the AI world. It is ideal for building and serving large-scale AI/ML applications and workloads. Cloud provides scalability, speed, and ease of deployment for machine learning that would otherwise be highly involved when done standalone. Let's discuss some advantages of using the Cloud for AI. First, we get on-demand compute resources, so if ML needs more horsepower, it can be acquired instantaneously. Cloud platforms provide a suite of products, like low-code, no-code builders, tools, CI/CD pipelines, serving platforms, and observability pipelines. They are already pre-wired, so it's easy to build, deploy, and manage ML services on the cloud. Cloud platforms are compliant to a number of security standards and benchmarks. They do offer enhanced security mechanisms to protect sensitive data. They provide global accessibility, so once deployed, an ML service can be accessed from…
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