Deliver high-performance ML models faster with MLOps tools

Machine learning operations (MLOps) tools help you automate and standardize the ML lifecycle to productionize ML models faster without compromising model performance. Amazon SageMaker provides a breadth of mission-ready MLOps tools to experiment, train, test, deploy, and govern ML models at scale. In this session, learn how to use SageMaker to implement an end-to-end MLOps solution for your organization.

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[svitlanatuchyna]

This tutorial is a good general overview of SageMaker for those who already have quite a lot of experience in ML. But it is definitely not suitable for beginners. The first part was easy to follow but the second part was more confusing.

[be_present_now]

@awssupport Could you please record the tutorials in higher resolutions preferebly 1080p so that code is crisply visible for a better learning experience. Thanks!

[chsaloua]

Thank you guys for the tutorial.
I had a problem implementing the scripts for SageMaker experiments. It appears that when working with SageMaker experiments, it's important to separate the training script from the SageMaker-specific code for managing experiments. So the "preprocessing.py" and "train.py" should not include any SageMaker-specific commands.

[GenAIML_senseNsimplicity]

The github code repository does not have 00_EDA_Local.ipynb, 01_DataPrep.ipynb etc files. Where to find these files?