From the course: Cloud Architecture: Core Concepts
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Cloud AI
From the course: Cloud Architecture: Core Concepts
Cloud AI
- [Instructor] AI systems rely on high performance computing for tasks like training machine learning models, and running AI algorithms in production. A cloud architecture must incorporate scalable and elastic compute resources, such as GPUs or TPU, to handle AI resource-intensive workloads efficiently. For example, serverless computing can provide flexible on demand infrastructure tailored to AI needs for GPU and TPU clusters that can be accessed on demand. Data is the foundation of AI. Cloud architectures must include robust data storage solutions like data lakes or object storage to handle large structured and unstructured data sets that are used to train the AI models. The architecture should also leverage data processing pipelines for cleaning, transforming, and loading data into machine learning workflows. Storage and processing must be designed to meet the AI requirements of performance latency and compliance with regulatory standards. Cloud providers offer integrated AI…
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