Why Jupyter Notebooks Can't Handle Production ML? #jupyternotebook #ml

In this video, we explore the critical differences between training jobs and Jupyter notebooks in machine learning workflows. While Jupyter notebooks are excellent for experimentation and interactive development, they fall short in productionization and distribution. Discover how training jobs enable scalability by deploying tasks across multiple cloud instances to handle larger datasets efficiently. We’ll also compare how training jobs are defined in Azure ML and SageMaker, ensuring you understand the best practices for your ML projects.

🗒️ Resources mentioned in the video:
- Full video guide: https://youtu.be/-ie4AEveM24
- Blog post: https://towardsdatascience.com/aws-vs-azure-a-deep-dive-into-model-training-part-2/

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Видео Why Jupyter Notebooks Can't Handle Production ML? #jupyternotebook #ml канала Visual Design Studio