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Training and Inference of LLMs with PyTorch Fully Sharded Data Parallel and Better Transformer

In this blog we show how to perform efficient and optimized distributed training and inference of large language models using PyTorch’s Fully Sharded Data Parallel and Better Transformer implementations, on the Spark platform. In this implementation, we combine Microsoft Fabric for data preparation and model inference, and Azure Databricks for model training, having all our […]

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Optimized Training and Inference of Hugging Face Models on Azure Databricks – Part 2

In this two-part blog series, we explore how to perform optimized training and inference of large language models from Hugging Face, at scale, on Azure Databricks.   In the first part we focused on optimized model training, leveraging the distributed parallel infrastructure available on Azure Databricks to train deep learning-based models, and using DeepSpeed to

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Optimized Training and Inference of Hugging Face Models on Azure Databricks – Part 1

In this two-part blog series, we explore how to perform optimized training and inference of large language models from Hugging Face, at scale, on Azure Databricks.   In this first part we focus on optimized model training, leveraging the distributed parallel infrastructure available on Azure Databricks to train deep learning-based models, and using DeepSpeed to

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Data Analytics and Data Virtualization with Azure Databricks and Microsoft SQL Server

A modern data analytics architecture centered on the Databricks platform implements what is known as a Data Lakehouse architecture. It integrates the traditional Data Lake architecture with some functionality that previously was only available to Data Warehouse platforms, such as advanced data management features and support to ACID transactions, schema enforcement, incremental data loading, among

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A Solution Template for Soft Sensor Modeling on Azure – Part 2

In this two-part blog series we explore a solution template for creating models for soft sensors, taking advantage of the scalability and automation provided by the Microsoft Azure platform.   In the first part, we explored what a soft sensor is, the use case, dataset, common approaches and major steps usually needed to model soft

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A Solution Template for Soft Sensor Modeling on Azure – Part 1

In this two-part blog series we explore a solution template for creating models for soft sensors, taking advantage of the scalability and operationalization provided by the Microsoft Azure platform. This is the first part, where we explore what a soft sensor is, the use case, dataset, common approaches and major steps usually needed to model

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Deep Learning with BERT on Azure ML for Text Classification

This is the second part of a two-part blog series, where we explore how to develop the machine learning model that powers our solution. In the first part we presented an end-to-end, AI-powered solution architecture to automate support tickets classification and discussed key details highlighting the usage of serverless and PaaS services in Microsoft Azure.

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Automated Service Ticket Routing with Deep Learning on Azure

In this two-part blog series, we explore a robust end-to-end architecture powered by modern deep learning techniques and built on Microsoft Azure to implement an automated service ticket routing solution. In the first part, we discuss key architectural details highlighting the usage of serverless and PaaS services in Microsoft Azure that allow the rapid implementation

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