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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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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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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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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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