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Tracking Financial Crime with Azure Confidential Computing and Sarus Smart Privacy Solution

Tracking Financial Crime with Azure Confidential Computing and Sarus Smart Privacy Solution Authors: Maxime Agostini, Lindsey Allen, and Wolfgang M. Pauli Combining Azure confidential computing capabilities with Sarus unlocks new possibilities to combine sensitive data from multiple parties. Working with multiple banks, we demonstrated how a joint solution can track financial crime by pooling transaction […]

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Extremely Fast Training of Extremely Small Text Classification Models with Azure SQL

In a previous blog post, we described how to fine-tune a pretrained Hugging Face transformer model for text classification at scale. We used a PyTorch dataset class for pulling data directly from a SQL database. The advantages of this approach were obvious, in comparison to loading the data into memory of the host machine at

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Fine-tuning a transformer model for question natural language inference (QNLI) with Azure SQL

Figure 1: An example of QNLI. The task of the model is to determine whether the sentence contains the information required to answer the question.   Introduction Question natural language inference (QNLI) can be described as determining whether a paragraph of text contains the necessary information for answering a question. There are many real-world applications

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Active Learning at scale, with Azure SQL and Azure ML

Figure 1: Demonstration of a deep learning model making sense of thousands of images by identifying their underlying categorical structure. Each dot represents the location of a sample image in the model’s semantic representation of the dataset, known as the embedding space. t-SNE was used to create this 2D projection, which shows the model’s representation

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Building a digital guide dog for railway passengers with impaired vision

  Background/Motivation Training a young student to find the doors on the Munich subway, using a white cane. Catching your train on time can be challenging under the best of circumstances. Trains typically only stop for a few minutes, leaving little room for mistakes. For example, at Munich Main station around 240 express trains and

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Reducing the distance to your Azure ML remote compute jobs

Under (hopefully) rare circumstances, after developing a training script and thorough local testing, it can still happen that the same script fails when executed on a remote AML compute target. Here, we are sharing some best practices around how to debug remote workloads on Azure ML. Debugging remote workloads can be broken down into two

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ML Inference on Edge devices with ONNX Runtime using Azure DevOps+MLOps

Authors: Wolfgang M. Pauli and Manash Goswami AI applications are designed to perform tasks that emulate human intelligence to make predictions that help us make better decisions for the scenario. This drives operational efficiency when the machine executes the task without worrying about fatigue or safety. But the effectiveness of the AI application is defined

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Compiling software dependencies with Azure ML Pipelines

In a previous blog post, I discussed various approaches to resolving complex software and hardware dependencies in Azure DevOps pipelines. In this blog post, I want to discuss an alternative, lightweight approach to the same problem: Using Azure Machine Learning (AML) Pipelines to compile software dependencies for model training and deployment. The use case is

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Accelerate your end-to-end ML lifecycles with semi-automated image annotation using Azure ML

Many data and machine learning scientists have had the experience of working on a computer vision problem, e.g. object detection, that requires a significant investment of time annotating images within an unlabeled dataset. While labeling data can be relaxing, our existence is impermanent. The following cloud-based solution speeds up the end-to-end ML lifecycle by performing

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Create your own customized translation service with Microsoft Custom Translator

Organizations and applications increasingly aim to cater to an international clientele. This often requires them to operate in various languages. For example, a multi-national corporation may have to translate internal documents for employees who live in different countries and speak diverse languages. The same company may also want to have their user manuals in various

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