From the course: Scalable Data Storage and Processing for AI Workloads

Scalable solutions for storage and processing

From the course: Scalable Data Storage and Processing for AI Workloads

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Scalable solutions for storage and processing

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- [Instructor] Hi, and welcome to this course on scalable data storage and processing for AI workloads. Now, before we dive into the contents of this course, let's understand why it's important to have scalable solutions, especially when you're working with AI model training, deployment, and inference. Now, in this world of generative AI, it shouldn't be surprising to anyone that AI needs a lot of data. Scalable AI storage solutions must ensure seamless accommodation of increasing data volumes, crucial as AI models rely on progressively larger data sets for improved training, inference, and overall performance. For example, the AI needed to train autonomous vehicles require immense data sets of road conditions, traffic patterns, and environmental scenarios to ensure that they can deal with different conditions. Scalable storage solutions are a must for optimized performance. Scalable storage supports high-speed data access and processing. It'll reduce latency and enable efficient handling of real-time analytics and complex AI workloads. For example, platforms like Amazon use AI for real-time recommendation systems, which means they require rapid access to large customer behavior data sets to deliver their personalized suggestions. While you scale storage, it's important that storage is cost effective as well. Organizations prefer to avoid over-provisioning and optimize costs while maintaining the flexibility to adapt to evolving AI project requirements. For example, a healthcare startup using AI for diagnostics can begin with smaller storage and scale up as their patient data grows, making sure that their costs are within their budget and also supporting future needs. AI is not just data hungry. It needs a lot of compute power as well. Scalable processing enables AI systems to handle increasing computational demands as models grow more complex and data sets expand. The phenomenal natural language processing models that we see today like GPT requires substantial computational power for training that scales significantly with larger data sets and advanced architectures. It's important that even though processing requirements grow, you have efficient allocation of resources and have reduced training and inference time for AI workloads. For instance, in weather prediction, scalable processing allows climate models to quickly analyze vast amounts of atmospheric data, allowing you to get timely, accurate forecasts. AI computation requires massive amounts of power, so it's important that you dynamically adjust processing power based on workload requirements to allow organizations to manage infrastructure costs while addressing energy-intensive demands of AI workloads.

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