课程: Understanding Generative AI in Cloud Computing: Services and Use Cases

Introduction to cloud-based storage and data management

课程: Understanding Generative AI in Cloud Computing: Services and Use Cases

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Introduction to cloud-based storage and data management

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- [Instructor] AI models specifically generative AI need a lot of resources. Storage and database resources are much more challenging to find and access in traditional systems. Thus, the use of cloud computing has revolutionized, not only our ability to leverage AI, but also our ability to make this technology accessible and affordable. At the core of cloud storage are the following main concepts. Cloud storage is how we store data and files on cloud storage systems. An example would be Amazon Web Services or AWS S3. Cloud Databases that leverage cloud storage provides structured access. An example of this would be AWS's RDS Database. Both are needed to provide the data and storage systems required to train generative AI in the cloud and manage the output of productive AI systems. Data as we'll learn in this course is the engine of generative AI in the cloud and data is needed to build knowledge models. In many instances, terabytes of information may fuel a single response of a generative AI engine in the cloud. Thus, we must understand this is a garbage in, garbage out relationship. Your cloud-based generative AI systems are only as intelligent as what they have been fed. We can see this using certain public AI systems, which often get things wrong or can reflect biases that already exist in society because of the data they've been modeled after. The same concept exists within most AI systems, and thus the training data being ingested should be carefully considered. If you'd like to learn more about cloud storage specifically, you can watch my other courses on Linkedin Learning, Learning Cloud Computing: Cloud Storage. Right now, we'll provide you with the fundamentals to understand storage within the context of generative AI systems in the cloud. There are many types of cloud storage, including object block and file storage types. What's important here is to remember that they are used to retain information long-term. Databases can leverage these cloud storage systems to organize how data is structured, making it easier for generative AI systems in the cloud to store and retrieve information for functions like training and the output of answers. The critical takeaway here is that cloud storage systems and cloud databases are the centers of the universe when it comes to all types of AI, including generative AI in the cloud. You can think of generative AI in the cloud as a car or a truck with data being the fuel that runs it. Keep in mind that cloud databases provide structure for cloud storage. An example would be tossing all types of books into the middle of the room versus putting them on shelves in order. The data is there in both cases, but it's easy to access if organized. Generative AI systems use data in much the same way. While this is just a short introduction to cloud storage and database management, the discipline itself is an essential component of generative AI systems.

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