30 Sep 2022| ONPASSIVE
All you need to know about Artificial Intelligence Beginners Guide – Part 12
Hey, everyone! Welcome back to AI beginners guide. Did you know: There’s a robot cook called Moley who can actually cook food by the time you come home. Wouldn’t that be great to have someone at your home cook food for you after a long day at work? It would be, right? Humans are making a lot of progress in the field of artificial intelligence and machine learning. We can see a new robot or an AI program being developed every day.
In our last ONPASSIVE AI blog – AI beginners guide – part 11, we saw how to integrate data for better AI implementation. Today, we are going to see the next important step after integrating data i.e., including Storage as Part of your AI Plan.
Once you ramp up from a small sample of data, you’ll need to consider the storage requirements to implement AI. And here’s how to do it:
Developing algorithms is important to achieve research results. But this is not possible without huge amounts of data to develop more accurate models; AI methods cannot improve enough to achieve your AI implementing objectives. That is why you need to have storage for AI that is fast and optimized. This should be considered at the start of AI system design.
AI and machine learning workloads require a specific storage factor that needs to be considered. These include:
Scalability: Machine learning needs organizations to process huge amount of data. But the more data volumes you process, the more linear improvements you’ll find in the AI models. Therefore in order to enhance the accuracy of AI and ML models, organizations must gather and store a huge amount of data every day.
Accessibility: There must be continuous access to data. AI and machine learning require the storage system to read and re-read the data, normally in a random fashion. This means an organization should have a separate storage system to help AI and machine learning systems to learn and analyse the data.
Latency: The latency of I/O is essential for building and using AI and machine learning systems because data is read and reread several times. Reducing the I/O latency can decrease the training time for AI systems by days or months. Faster model development helps build greater business advantage.
AI and machine learning are all about data, with the huge amount of data comes the need for bigger storage structures. It is important to have a high-performance and highly scalable storage system to assist AI to perform well.
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