At Flash AI, we are using CloudDat to build, fine-tune, and deploy multi-modal AI workloads and frameworks into our inference pipeline. Our compute requirements are extremely spiky, with instances where we need significant resources and other times when we do not. The distributed model training and data pre-processing clusters help us run intensively for four to twelve hours, followed by idle or lower workloads. We have implemented CloudDat on an hourly consumption model so that we have scalable data orchestration which is managed, with object ingestion included. We basically use it without doing a monthly fixed infrastructure allocation, which is our main use case. The challenges are primarily due to the volumes coming in. Since we operate on an hourly metering system, we are seeing trends and optimizing per-hour metering. When there is a need, we scale up. When there is not a need and the volumes are low or the workload is low, we opt for less load or no load. This is how we manage that.
My main use case for CloudDat is storing, processing, and analyzing data. At my project and company, we use platforms like AWS instead of some servers. This really helps when data is handled in real-time. CloudDat's real power comes into picture in real-time scenarios where anyone can use cloud in a very safe way. Cloud is basically referred to as a more secure method. That's where in the real-time scenario, we use it. I work in healthcare as of now, so my scenario with CloudDat is more into wearable devices. For example, for any patient, they have to check heart rate or any oxygen level. We have scenarios where we have wearable devices to stream patients' vitals. For that, we use these data flows in a cloud dashboard where doctors generally use this data flow, and that really benefits the health risk of that particular patient. As of now in the health industry, this is one of the examples where I really come into the picture, and I have hands-on experience with CloudDat. I don't think I have any other good example other than this.
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At Flash AI, we are using CloudDat to build, fine-tune, and deploy multi-modal AI workloads and frameworks into our inference pipeline. Our compute requirements are extremely spiky, with instances where we need significant resources and other times when we do not. The distributed model training and data pre-processing clusters help us run intensively for four to twelve hours, followed by idle or lower workloads. We have implemented CloudDat on an hourly consumption model so that we have scalable data orchestration which is managed, with object ingestion included. We basically use it without doing a monthly fixed infrastructure allocation, which is our main use case. The challenges are primarily due to the volumes coming in. Since we operate on an hourly metering system, we are seeing trends and optimizing per-hour metering. When there is a need, we scale up. When there is not a need and the volumes are low or the workload is low, we opt for less load or no load. This is how we manage that.
My main use case for CloudDat is storing, processing, and analyzing data. At my project and company, we use platforms like AWS instead of some servers. This really helps when data is handled in real-time. CloudDat's real power comes into picture in real-time scenarios where anyone can use cloud in a very safe way. Cloud is basically referred to as a more secure method. That's where in the real-time scenario, we use it. I work in healthcare as of now, so my scenario with CloudDat is more into wearable devices. For example, for any patient, they have to check heart rate or any oxygen level. We have scenarios where we have wearable devices to stream patients' vitals. For that, we use these data flows in a cloud dashboard where doctors generally use this data flow, and that really benefits the health risk of that particular patient. As of now in the health industry, this is one of the examples where I really come into the picture, and I have hands-on experience with CloudDat. I don't think I have any other good example other than this.