From the course: Understanding Generative AI in Cloud Computing: Services and Use Cases

Introduction to cloud-based storage and data management

From the course: 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. This means that the use of cloud computing has revolutionized not only the 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, which is just what it sounds like, it's how we store and retrieve data files on remote servers accessible through the internet. An example would be Amazon Web Services S3. Cloud databases that leverage cloud storage provide structured access. An example of this would be AWS's RDS database. Both are needed to store and manage the data and systems used to train generative AI in the cloud, as well as manage the output of productive AI systems. Data is the engine of generative AI systems 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. Because of this, it's important to understand that this is a garbage in, garbage out relationship. Your cloud-based generative AI systems are only as intelligent as what they've been fed. We can see this one 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 off of. 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 such as LinkedIn Learning Cloud Computing: Cloud Storage. For 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 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 output of answers. The critical takeaway here is that cloud storage systems and cloud databases are the center 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 the 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 easier to access if it's organized. Generative AI systems use data in much the same manner. While this is just a short introduction to cloud storage and cloud database management, the discipline itself is an essential component of generative AI. It could not be useful without data leverage to train the knowledge models, which means both parts must function correctly for generative AI in the cloud to provide any value.

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