Pre-train and Fine-tune Language Model with Hugging Face and Gaudi HPU.
In this blog, we provide a general guideline of pre-training and fine-tuning language models using Hugging Face. For illustration, we use pre-training language models for question generation (answer-agnostic) in Korean as running example.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.
Deploying Azure Machine Learning service models for inference with Azure Functions
Deploying Azure Machine Learning service models for inference with Azure Functions Why customers want Azure Functions integrated with Azure ML Service? There is no need to have pre-provisioned resources such as clusters Leverage event driven programming with several built-in triggers – for example, when the payload is uploaded to Storage, automatically trigger the Azure Function to score the payload using the AML model. There is no need to remember URLs or IP addresses to make POST requests.
Introduction to Azure DevOps for Machine Learning
One of the biggest challenges with integrating AI into an application is getting the model deployed into a production environment and keeping it operational/supportable. DevOps for Machine Learning can be streamlined with visibility into training, experiment metrics, and model versions. Azure Machine Learning service allows you to seamlessly integrate with Azure services to provide end-to-end capabilities for the entire Machine Learning lifecycle, making it simpler and faster than ever.
