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Real-Time Transport and Stream Processing You also want to look into recent in-app fraud, because there might be a trend, and maybe this specific transaction is related to those other fraudulent transactions.Ī lot of this is recent information, and the question is: how do you quickly assess these recent features? You don't want to move the data in and out of your permanent storage because it might take too long and users are impatient. You also need to know about the specific credit card’s recent transactions, because when a credit card is stolen, the thief wants to make the most out of that credit card by using it for multiple transactions at the same time. To detect whether a transaction is fraudulent, you want information about that transaction specifically as well as the user's other recent transactions.
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To illustrate a real-time pipeline, imagine you're building a fraud detection model for a ride-sharing service like Uber or Lyft. What does that mean? A pipeline that can process data, input the data into the model, and generate predictions and return predictions in real time to users. However, the solution I recommend is to use a real-time pipeline. You could also use more powerful hardware, which allows models to do computation faster. One solution is to use model compression techniques such as quantization and distillation. The first is a model that is capable of returning fast inference. How do you make online prediction work? You actually need two components. These give better accuracy, but it generally also means that inference takes longer, and users don't want to wait. A common trend in ML is toward bigger models. Research shows that no matter how good your model predictions are, if it takes even a few milliseconds too long to return results, users will leave your site or click on something else. The problem with online predictions is latency. Improve productivity and collaboration with unparalleled speed, scale, and resilience. Camunda Platform 8: The Universal Process Orchestrator.