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Learn how to take your machine learning model from experimentation to production by using Azure Machine Learning jobs.

With the Azure Machine Learning interactive job experience, data scientists can use the Azure Machine Learning Python SDK, Azure Machine Learning CLI or the Azure Studio to access the container where their job is running. Once the job container is accessed, users can iterate on training scripts, monitor training progress or debug the job remotely like they typically do on their local machines.

Senior Applied Research Scientist - Machine Learning and Generative AI - Microsoft Research · Redmond, WA · Be an early applicant 6 days ago · Applied Researcher - GenAI · United States · Actively Hiring 4 days ago · Cloud Machine Learning Engineer - US remote · United States 8 months ago · Principal Data Scientist · New York, NY · Actively Hiring 3 weeks ago · Research Intern - Azure AI: Machine Translation ·

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Salary Search: Azure AI Infrastructure Specialist salaries in Remote · Familiarity with machine learning concepts and model training pipelines. You will leverage your expertise in Azure Machine Learning and MLOps best practices to… · View all aNumak & Company jobs in Wilmington, DE - Wilmington ...

To utilize Azure Machine Learning's TensorBoard integration, make sure you write your TensorBoard logs to this folder. While your job is in progress, you'll be able to launch TensorBoard and stream these logs. Later, you'll also be able to restore the logs from any of your previous jobs. For example, to download a file written to the outputs folder to your local machine after your remote training job: run.download_file(name='outputs/my_output_file', output_file_path='my_destination_path')

You can train models by using the Azure CLI, the REST API, or you can use the studio UI to directly create a training job. In this article, you learn how to use your own data and code to train a machine learning model with a guided experience ...

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The YAML reference docs for job can be found at: https://aka.ms/ml-cli-v2-job-command-yaml-reference, https://aka.ms/ml-cli-v2-job-sweep-yaml-reference, https://aka.ms/ml-cli-v2-job-pipeline-yaml-reference. ... Name of resource group. You can configure the default group using az configure --defaults group=<name>. ... Name of the Azure ML workspace.

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Configure and submit Azure Machine Learning jobs to train your models using the SDK, CLI, etc.

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Your machine learning (ML) job history is an important part of an explainable and repeatable ML development process. ... Add a job display name. Create a custom view. Add a job description. Tag and find jobs. Run search over your job history. Cancel or fail jobs. Monitor job status by email notification. Monitor your job resources (preview). ... For information on using the Azure Machine Learning SDK v1 or CLI v1, see Monitor and analyze jobs in studio (v1).

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