{"id":872,"date":"2019-11-18T00:00:00","date_gmt":"2019-11-18T00:00:00","guid":{"rendered":"https:\/\/azure.microsoft.com\/blog\/pytorch-on-azure-with-streamlined-ml-lifecycle"},"modified":"2025-06-26T23:50:32","modified_gmt":"2025-06-27T06:50:32","slug":"pytorch-on-azure-with-streamlined-ml-lifecycle","status":"publish","type":"post","link":"https:\/\/azure.microsoft.com\/en-us\/blog\/pytorch-on-azure-with-streamlined-ml-lifecycle\/","title":{"rendered":"PyTorch on Azure with streamlined ML lifecycle"},"content":{"rendered":"\n

It’s exciting to see the Pytorch Community continue to grow and regularly release updated versions of PyTorch! Recent releases improve<\/a> performance, ONNX export, TorchScript, C++ frontend, JIT, and distributed training. Several new experimental features, such as quantization, have also been introduced.<\/p>\n\n\n\n

At the PyTorch Developer Conference earlier this fall, we presented how our open source contributions to PyTorch<\/a> make it better for everyone in the community. We also talked about how Microsoft uses PyTorch<\/a> to develop machine learning models for services like Bing. Whether you are an individual, a small team, or a large enterprise, managing the machine learning lifecycle can be challenging. We’d like to show you how Azure Machine Learning<\/a> can make you and your organization more productive with PyTorch.<\/p>\n\n\n\n

Streamlining the research to production lifecycle with Azure Machine Learning<\/h2>\n\n\n\n

One of the benefits of using PyTorch 1.3 in Azure Machine Learning is Machine Learning Operations (MLOps). MLOps streamlines the end-to-end machine learning (ML) lifecycle so you can frequently update models, test new models, and continuously roll out new ML models alongside your other applications and services. MLOps provides:<\/p>\n\n\n\n