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Continuous integration and continuous delivery (CI/CD) pipeline – Using the customer’s GitHub repository enabled code versioning and automated scripts to launch pipeline deployment whenever new versions of the code are committed. The drift notification emails will look similar to the examples in Figure 8.
To demonstrate the orchestrated workflow, we use an example dataset regarding diabetic patient readmission. You can try out the approach with this example and experiment with additional data transformations following similar steps with your own datasets. For more information, refer to Amazon SageMaker Identity-Based Policy Examples.
The majority of enterprise customers already have a well-established MLOps practice with a standardized environment in place—for example, a standardized repository, infrastructure, and security guardrails—and want to extend their MLOps process to no-code and low-code AutoML tools as well. For this post, you use a CloudFormation template.
If you have a different format, you can potentially use Llama convert scripts or Mistral convert scripts to convert your model to a supported format. The fine-tuning scripts are based on the scripts provided by the Llama fine-tuning repository. from sagemaker.s3 3B model Now, we’ll fine-tune the Llama 3.2 from sagemaker.s3
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